Publications of the Knowledge & Data Engineering Group

The list is generated automatically by BibSonomy. For more details and BibTeX entries, please click on the titles.

2024

• 1.

  Draude, C., Engert, S., Hess, T., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M., Zwingmann, N.: Verrechnung – Design – Kultivierung: Instrumentenkasten für die Gestaltung fairer Geschäftsmodelle durch Ko-Valuation, https://plattform-privatheit.de/p-prv-wAssets/Assets/Veroeffentlichungen_WhitePaper_PolicyPaper/whitepaper/WP_2024_FAIRDIENSTE_1.0.pdf, (2024). https://doi.org/10.24406/publica-2497.

  URLBibTeXEndNoteDOIBibSonomy

  @misc{claude2024verrechnung,
  address = {Karlsruhe},
  author = {Draude, Claude and Engert, Simon and Hess, Thomas and Hirth, Johannes and Horn, Viktoria and Kropf, Jonathan and Lamla, Jörn and Stumme, Gerd and Uhlmann, Markus and Zwingmann, Nina},
  edition = 1,
  editor = {Friedewald, Michael and Roßnagel, Alexander and Geminn, Christian and Karaboga, Murat},
  howpublished = {White Paper},
  keywords = {itegpub},
  month = {03},
  publisher = {Fraunhofer-Institut für System- und Innovationsforschung ISI},
  series = {Plattform Privatheit},
  title = {Verrechnung – Design – Kultivierung: Instrumentenkasten für die Gestaltung fairer Geschäftsmodelle durch Ko-Valuation},
  year = 2024
  }
  

  %0 Generic
  %1 claude2024verrechnung
  %A Draude, Claude
  %A Engert, Simon
  %A Hess, Thomas
  %A Hirth, Johannes
  %A Horn, Viktoria
  %A Kropf, Jonathan
  %A Lamla, Jörn
  %A Stumme, Gerd
  %A Uhlmann, Markus
  %A Zwingmann, Nina
  %B Plattform Privatheit
  %C Karlsruhe
  %D 2024
  %E Friedewald, Michael
  %E Roßnagel, Alexander
  %E Geminn, Christian
  %E Karaboga, Murat
  %I Fraunhofer-Institut für System- und Innovationsforschung ISI
  %R 10.24406/publica-2497
  %T Verrechnung – Design – Kultivierung: Instrumentenkasten für die Gestaltung fairer Geschäftsmodelle durch Ko-Valuation
  %U https://plattform-privatheit.de/p-prv-wAssets/Assets/Veroeffentlichungen_WhitePaper_PolicyPaper/whitepaper/WP_2024_FAIRDIENSTE_1.0.pdf
  %7 1
  

• 1.

  Hirth, J., Hanika, T.: The Geometric Structure of Topic Models, (2024).

  BibTeXEndNoteBibSonomy

  Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic models are based on simple visualizations, such as similarity matrices, top-term lists or embeddings, which are limited to a maximum of three dimensions. In this paper, we propose an incidence-geometric method for deriving an ordinal structure from flat topic models, such as non-negative matrix factorization. These enable the analysis of the topic model in a higher (order) dimension and the possibility of extracting conceptual relationships between several topics at once. Due to the use of conceptual scaling, our approach does not introduce any artificial topical relationships, such as artifacts of feature compression. Based on our findings, we present a new visualization paradigm for concept hierarchies based on ordinal motifs. These allow for a top-down view on topic spaces. We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.

  @preprint{hirth2024geometric,
  abstract = {Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic models are based on simple visualizations, such as similarity matrices, top-term lists or embeddings, which are limited to a maximum of three dimensions. In this paper, we propose an incidence-geometric method for deriving an ordinal structure from flat topic models, such as non-negative matrix factorization. These enable the analysis of the topic model in a higher (order) dimension and the possibility of extracting conceptual relationships between several topics at once. Due to the use of conceptual scaling, our approach does not introduce any artificial topical relationships, such as artifacts of feature compression. Based on our findings, we present a new visualization paradigm for concept hierarchies based on ordinal motifs. These allow for a top-down view on topic spaces. We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.},
  author = {Hirth, Johannes and Hanika, Tom},
  keywords = {kde},
  title = {The Geometric Structure of Topic Models},
  year = 2024
  }
  

  %0 Generic
  %1 hirth2024geometric
  %A Hirth, Johannes
  %A Hanika, Tom
  %D 2024
  %T The Geometric Structure of Topic Models
  %X Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic models are based on simple visualizations, such as similarity matrices, top-term lists or embeddings, which are limited to a maximum of three dimensions. In this paper, we propose an incidence-geometric method for deriving an ordinal structure from flat topic models, such as non-negative matrix factorization. These enable the analysis of the topic model in a higher (order) dimension and the possibility of extracting conceptual relationships between several topics at once. Due to the use of conceptual scaling, our approach does not introduce any artificial topical relationships, such as artifacts of feature compression. Based on our findings, we present a new visualization paradigm for concept hierarchies based on ordinal motifs. These allow for a top-down view on topic spaces. We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.
  

• 1.

  Hille, T., Stubbemann, M., Hanika, T.: Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research. Transactions on Machine Learning Research. (2024).

  URLBibTeXEndNoteBibSonomy

  @article{hille2024reproducibility,
  author = {Hille, Tobias and Stubbemann, Maximilian and Hanika, Tom},
  journal = {Transactions on Machine Learning Research},
  keywords = {itegpub},
  note = {Reproducibility Certification},
  title = {Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research.},
  year = 2024
  }
  

  %0 Journal Article
  %1 hille2024reproducibility
  %A Hille, Tobias
  %A Stubbemann, Maximilian
  %A Hanika, Tom
  %D 2024
  %J Transactions on Machine Learning Research
  %T Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research.
  %U https://openreview.net/forum?id=CtEGxIqtud
  

• 1.

  Dürrschnabel, D., Priss, U.: Realizability of Rectangular Euler Diagrams, (2024).

  BibTeXEndNoteBibSonomy

  @misc{dürrschnabel2024realizability,
  author = {Dürrschnabel, Dominik and Priss, Uta},
  keywords = {itegpub},
  title = {Realizability of Rectangular Euler Diagrams},
  year = 2024
  }
  

  %0 Generic
  %1 dürrschnabel2024realizability
  %A Dürrschnabel, Dominik
  %A Priss, Uta
  %D 2024
  %T Realizability of Rectangular Euler Diagrams
  

• 1.

  Hirth, J.: Conceptual Data Scaling in Machine Learning, (2024). https://doi.org/10.17170/kobra-2024100910940.

  BibTeXEndNoteDOIBibSonomy

  Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.

  @phdthesis{doi:10.17170/kobra-2024100910940,
  abstract = {Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.},
  author = {Hirth, Johannes},
  keywords = {Knowldege~Representation},
  school = {Kassel, Universität Kassel, Fachbereich Elektrotechnik/Informatik},
  title = {Conceptual Data Scaling in Machine Learning},
  year = 2024
  }
  

  %0 Thesis
  %1 doi:10.17170/kobra-2024100910940
  %A Hirth, Johannes
  %D 2024
  %R 10.17170/kobra-2024100910940
  %T Conceptual Data Scaling in Machine Learning
  %X Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.
  

• 1.

  Horn, V., Hirth, J., Holfeld, J., Behmenburg, J.H., Draude, C., Stumme, G.: Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content. In: Nordic Conference on Human-Computer Interaction. Association for Computing Machinery, Uppsala, Sweden (2024). https://doi.org/10.1145/3679318.3685414.

  URLBibTeXEndNoteDOIBibSonomy

  Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user’s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user’s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.

  @inproceedings{hci-lattice,
  abstract = {Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user’s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user’s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.},
  address = {New York, NY, USA},
  author = {Horn, Viktoria and Hirth, Johannes and Holfeld, Julian and Behmenburg, Jens Hendrik and Draude, Claude and Stumme, Gerd},
  booktitle = {Nordic Conference on Human-Computer Interaction},
  keywords = {itegpub},
  publisher = {Association for Computing Machinery},
  series = {NordiCHI 2024},
  title = {Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content},
  year = 2024
  }
  

  %0 Conference Paper
  %1 hci-lattice
  %A Horn, Viktoria
  %A Hirth, Johannes
  %A Holfeld, Julian
  %A Behmenburg, Jens Hendrik
  %A Draude, Claude
  %A Stumme, Gerd
  %B Nordic Conference on Human-Computer Interaction
  %C New York, NY, USA
  %D 2024
  %I Association for Computing Machinery
  %R 10.1145/3679318.3685414
  %T Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content
  %U https://doi.org/10.1145/3679318.3685414
  %X Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user’s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user’s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.
  %@ 9798400709661
  

• 1.

  Abdulla, M., Hirth, J., Stumme, G.: The Birkhoff Completion of Finite Lattices. In: Cabrera, I.P., Ferré, S., and Obiedkov, S. (eds.) Conceptual Knowledge Structures. pp. 20–35. Springer Nature Switzerland, Cham (2024).

  BibTeXEndNoteBibSonomy

  We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.

  @inproceedings{10.1007/978-3-031-67868-4_2,
  abstract = {We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.},
  address = {Cham},
  author = {Abdulla, Mohammad and Hirth, Johannes and Stumme, Gerd},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cabrera, Inma P. and Ferré, Sébastien and Obiedkov, Sergei},
  keywords = {itegpub},
  pages = {20--35},
  publisher = {Springer Nature Switzerland},
  title = {The Birkhoff Completion of Finite Lattices},
  year = 2024
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-67868-4_2
  %A Abdulla, Mohammad
  %A Hirth, Johannes
  %A Stumme, Gerd
  %B Conceptual Knowledge Structures
  %C Cham
  %D 2024
  %E Cabrera, Inma P.
  %E Ferré, Sébastien
  %E Obiedkov, Sergei
  %I Springer Nature Switzerland
  %P 20--35
  %T The Birkhoff Completion of Finite Lattices
  %X We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.
  %@ 978-3-031-67868-4
  

• 1.

  Draude, C., Dürrschnabel, D., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M.: Conceptual Mapping of Controversies. In: Cabrera, I.P., Ferré, S., and Obiedkov, S. (eds.) Conceptual Knowledge Structures. pp. 201–216. Springer Nature Switzerland, Cham (2024).

  BibTeXEndNoteBibSonomy

  With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles.

  @inproceedings{10.1007/978-3-031-67868-4_14,
  abstract = {With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles.},
  address = {Cham},
  author = {Draude, Claude and Dürrschnabel, Dominik and Hirth, Johannes and Horn, Viktoria and Kropf, Jonathan and Lamla, J{ö}rn and Stumme, Gerd and Uhlmann, Markus},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cabrera, Inma P. and Ferré, Sébastien and Obiedkov, Sergei},
  keywords = {itegpub},
  pages = {201--216},
  publisher = {Springer Nature Switzerland},
  title = {Conceptual Mapping of Controversies},
  year = 2024
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-67868-4_14
  %A Draude, Claude
  %A Dürrschnabel, Dominik
  %A Hirth, Johannes
  %A Horn, Viktoria
  %A Kropf, Jonathan
  %A Lamla, J{ö}rn
  %A Stumme, Gerd
  %A Uhlmann, Markus
  %B Conceptual Knowledge Structures
  %C Cham
  %D 2024
  %E Cabrera, Inma P.
  %E Ferré, Sébastien
  %E Obiedkov, Sergei
  %I Springer Nature Switzerland
  %P 201--216
  %T Conceptual Mapping of Controversies
  %X With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles.
  %@ 978-3-031-67868-4
  

• 1.

  Hanika, T., Hille, T.: What is the Intrinsic Dimension of Your Binary Data? - and How to Compute it Quickly. In: Cabrera, I.P., Ferr{{é}}, S., and Obiedkov, S.A. (eds.) Conceptual Knowledge Structures - First International Joint Conference, {CONCEPTS} 2024, C{{á}}diz, Spain, September 9-13, 2024, Proceedings. pp. 97–112. Springer (2024). https://doi.org/10.1007/978-3-031-67868-4\_7.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/concepts/HanikaH24,
  author = {Hanika, Tom and Hille, Tobias},
  booktitle = {Conceptual Knowledge Structures - First International Joint Conference, {CONCEPTS} 2024, C{{á}}diz, Spain, September 9-13, 2024, Proceedings},
  editor = {Cabrera, Inma P. and Ferr{{é}}, S{{é}}bastien and Obiedkov, Sergei A.},
  keywords = {itegpub},
  pages = {97--112},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {What is the Intrinsic Dimension of Your Binary Data? - and How to Compute it Quickly},
  volume = 14914,
  year = 2024
  }
  

  %0 Conference Paper
  %1 DBLP:conf/concepts/HanikaH24
  %A Hanika, Tom
  %A Hille, Tobias
  %B Conceptual Knowledge Structures - First International Joint Conference, {CONCEPTS} 2024, C{{á}}diz, Spain, September 9-13, 2024, Proceedings
  %D 2024
  %E Cabrera, Inma P.
  %E Ferr{{é}}, S{{é}}bastien
  %E Obiedkov, Sergei A.
  %I Springer
  %P 97--112
  %R 10.1007/978-3-031-67868-4\_7
  %T What is the Intrinsic Dimension of Your Binary Data? - and How to Compute it Quickly
  %U https://doi.org/10.1007/978-3-031-67868-4\_7
  %V 14914
  

• 1.

  Hanika, T., Jäschke, R.: A Repository for Formal Contexts. In: Proceedings of the 1st International Joint Conference on Conceptual Knowledge Structures (2024).

  URLBibTeXEndNoteBibSonomy

  Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this.

  @inproceedings{hanika2024repository,
  abstract = {Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this.},
  author = {Hanika, Tom and Jäschke, Robert},
  booktitle = {Proceedings of the 1st International Joint Conference on Conceptual Knowledge Structures},
  keywords = {repository},
  title = {A Repository for Formal Contexts},
  year = 2024
  }
  

  %0 Conference Paper
  %1 hanika2024repository
  %A Hanika, Tom
  %A Jäschke, Robert
  %B Proceedings of the 1st International Joint Conference on Conceptual Knowledge Structures
  %D 2024
  %T A Repository for Formal Contexts
  %U https://arxiv.org/abs/2404.04344
  %X Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this.
  

• 1.

  Hirth, J., Horn, V., Stumme, G., Hanika, T.: Ordinal motifs in lattices. Information Sciences. 659, 120009 (2024). https://doi.org/https://doi.org/10.1016/j.ins.2023.120009.

  URLBibTeXEndNoteDOIBibSonomy

  @article{HIRTH2024120009,
  author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom},
  journal = {Information Sciences},
  keywords = {itegpub},
  pages = 120009,
  title = {Ordinal motifs in lattices},
  volume = 659,
  year = 2024
  }
  

  %0 Journal Article
  %1 HIRTH2024120009
  %A Hirth, Johannes
  %A Horn, Viktoria
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2024
  %J Information Sciences
  %P 120009
  %R https://doi.org/10.1016/j.ins.2023.120009
  %T Ordinal motifs in lattices
  %U https://www.sciencedirect.com/science/article/pii/S0020025523015943
  %V 659
  

2023

• 1.

  Stubbemann, M., Hille, T., Hanika, T.: Selecting Features by their Resilience to the Curse of Dimensionality. (2023).

  BibTeXEndNoteBibSonomy

  @article{stubbemann2023selecting,
  author = {Stubbemann, Maximilian and Hille, Tobias and Hanika, Tom},
  keywords = {selecting},
  title = {Selecting Features by their Resilience to the Curse of Dimensionality},
  year = 2023
  }
  

  %0 Journal Article
  %1 stubbemann2023selecting
  %A Stubbemann, Maximilian
  %A Hille, Tobias
  %A Hanika, Tom
  %D 2023
  %T Selecting Features by their Resilience to the Curse of Dimensionality
  

• 1.

  Budde, K.B., Rellstab, C., Heuertz, M., Gugerli, F., Hanika, T., Verdú, M., Pausas, J.G., González-Martínez, S.C.: Divergent selection in a Mediterranean pine on local spatial scales. Journal of Ecology. n/a, (2023). https://doi.org/https://doi.org/10.1111/1365-2745.14231.

  URLBibTeXEndNoteDOIBibSonomy

  Abstract The effects of selection on an organism's genome are hard to detect on small spatial scales, as gene flow can swamp signatures of local adaptation. Therefore, most genome scans to detect signatures of environmental selection are performed on large spatial scales; however, divergent selection on the local scale (e.g. between contrasting soil conditions) has also been demonstrated, in particular for herbaceous plants. Here, we hypothesised that in topographically complex landscapes, microenvironment variability is strong enough to leave a selective footprint in the genomes of long-lived organisms. To test this, we investigated paired south- versus north-facing Pinus pinaster stands on the local scale, with trees growing in close vicinity (≤820 m distance between paired south- and north-facing stands), in a Mediterranean mountain area. While trees on north-facing slopes experience less radiation, trees on south-facing slopes suffer from especially harsh conditions, particularly during the dry summer season. Two outlier analyses consistently revealed five putatively adaptive loci (out of 4034), in candidate genes two of which encoded non-synonymous substitutions. Additionally, one locus showed consistent allele frequency differences in all three stand pairs indicating divergent selection despite high gene flow on the local scale. Permutation tests demonstrated that our findings were robust. Functional annotation of these candidate genes revealed biological functions related to abiotic stress response, such as water availability, in other plant species. Synthesis. Our study highlights how divergent selection in heterogeneous microenvironments shapes and maintains the functional genetic variation within populations of long-lived forest tree species, being the first to focus on adaptive genetic divergence between south- and north-facing slopes within continuous forest stands. This is especially relevant in the current context of climate change, as this variation is at the base of plant population responses to future climate.

  @article{https://doi.org/10.1111/1365-2745.14231,
  abstract = {Abstract The effects of selection on an organism's genome are hard to detect on small spatial scales, as gene flow can swamp signatures of local adaptation. Therefore, most genome scans to detect signatures of environmental selection are performed on large spatial scales; however, divergent selection on the local scale (e.g. between contrasting soil conditions) has also been demonstrated, in particular for herbaceous plants. Here, we hypothesised that in topographically complex landscapes, microenvironment variability is strong enough to leave a selective footprint in the genomes of long-lived organisms. To test this, we investigated paired south- versus north-facing Pinus pinaster stands on the local scale, with trees growing in close vicinity (≤820 m distance between paired south- and north-facing stands), in a Mediterranean mountain area. While trees on north-facing slopes experience less radiation, trees on south-facing slopes suffer from especially harsh conditions, particularly during the dry summer season. Two outlier analyses consistently revealed five putatively adaptive loci (out of 4034), in candidate genes two of which encoded non-synonymous substitutions. Additionally, one locus showed consistent allele frequency differences in all three stand pairs indicating divergent selection despite high gene flow on the local scale. Permutation tests demonstrated that our findings were robust. Functional annotation of these candidate genes revealed biological functions related to abiotic stress response, such as water availability, in other plant species. Synthesis. Our study highlights how divergent selection in heterogeneous microenvironments shapes and maintains the functional genetic variation within populations of long-lived forest tree species, being the first to focus on adaptive genetic divergence between south- and north-facing slopes within continuous forest stands. This is especially relevant in the current context of climate change, as this variation is at the base of plant population responses to future climate.},
  author = {Budde, Katharina B. and Rellstab, Christian and Heuertz, Myriam and Gugerli, Felix and Hanika, Tom and Verdú, Miguel and Pausas, Juli G. and González-Martínez, Santiago C.},
  journal = {Journal of Ecology},
  keywords = {itegpub},
  number = {n/a},
  title = {Divergent selection in a Mediterranean pine on local spatial scales},
  volume = {n/a},
  year = 2023
  }
  

  %0 Journal Article
  %1 https://doi.org/10.1111/1365-2745.14231
  %A Budde, Katharina B.
  %A Rellstab, Christian
  %A Heuertz, Myriam
  %A Gugerli, Felix
  %A Hanika, Tom
  %A Verdú, Miguel
  %A Pausas, Juli G.
  %A González-Martínez, Santiago C.
  %D 2023
  %J Journal of Ecology
  %N n/a
  %R https://doi.org/10.1111/1365-2745.14231
  %T Divergent selection in a Mediterranean pine on local spatial scales
  %U https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2745.14231
  %V n/a
  %X Abstract The effects of selection on an organism's genome are hard to detect on small spatial scales, as gene flow can swamp signatures of local adaptation. Therefore, most genome scans to detect signatures of environmental selection are performed on large spatial scales; however, divergent selection on the local scale (e.g. between contrasting soil conditions) has also been demonstrated, in particular for herbaceous plants. Here, we hypothesised that in topographically complex landscapes, microenvironment variability is strong enough to leave a selective footprint in the genomes of long-lived organisms. To test this, we investigated paired south- versus north-facing Pinus pinaster stands on the local scale, with trees growing in close vicinity (≤820 m distance between paired south- and north-facing stands), in a Mediterranean mountain area. While trees on north-facing slopes experience less radiation, trees on south-facing slopes suffer from especially harsh conditions, particularly during the dry summer season. Two outlier analyses consistently revealed five putatively adaptive loci (out of 4034), in candidate genes two of which encoded non-synonymous substitutions. Additionally, one locus showed consistent allele frequency differences in all three stand pairs indicating divergent selection despite high gene flow on the local scale. Permutation tests demonstrated that our findings were robust. Functional annotation of these candidate genes revealed biological functions related to abiotic stress response, such as water availability, in other plant species. Synthesis. Our study highlights how divergent selection in heterogeneous microenvironments shapes and maintains the functional genetic variation within populations of long-lived forest tree species, being the first to focus on adaptive genetic divergence between south- and north-facing slopes within continuous forest stands. This is especially relevant in the current context of climate change, as this variation is at the base of plant population responses to future climate.
  

• 1.

  Felde, M., Stumme, G.: Interactive collaborative exploration using incomplete contexts. Data & Knowledge Engineering. 143, 102104 (2023). https://doi.org/10.1016/j.datak.2022.102104.

  URLBibTeXEndNoteDOIBibSonomy

  @article{Felde_2023,
  author = {Felde, Maximilian and Stumme, Gerd},
  journal = {Data & Knowledge Engineering},
  keywords = {itegpub},
  month = {01},
  pages = 102104,
  publisher = {Elsevier {BV}},
  title = {Interactive collaborative exploration using incomplete contexts},
  volume = 143,
  year = 2023
  }
  

  %0 Journal Article
  %1 Felde_2023
  %A Felde, Maximilian
  %A Stumme, Gerd
  %D 2023
  %I Elsevier {BV}
  %J Data & Knowledge Engineering
  %P 102104
  %R 10.1016/j.datak.2022.102104
  %T Interactive collaborative exploration using incomplete contexts
  %U https://doi.org/10.1016%2Fj.datak.2022.102104
  %V 143
  

• 1.

  Stubbemann, M., Stumme, G.: The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks. In: Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, {ECML} {PKDD} 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part {III}. pp. 177–192. Springer (2023). https://doi.org/10.1007/978-3-031-43418-1\_11.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/pkdd/StubbemannS23,
  author = {Stubbemann, Maximilian and Stumme, Gerd},
  booktitle = {Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, {ECML} {PKDD} 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part {III}},
  keywords = {itegpub},
  pages = {177--192},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks},
  volume = 14171,
  year = 2023
  }
  

  %0 Conference Paper
  %1 DBLP:conf/pkdd/StubbemannS23
  %A Stubbemann, Maximilian
  %A Stumme, Gerd
  %B Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, {ECML} {PKDD} 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part {III}
  %D 2023
  %I Springer
  %P 177--192
  %R 10.1007/978-3-031-43418-1\_11
  %T The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks
  %U https://doi.org/10.1007/978-3-031-43418-1_11
  %V 14171
  

• 1.

  Hirth, J., Horn, V., Stumme, G., Hanika, T.: Ordinal Motifs in Lattices, https://arxiv.org/abs/2304.04827, (2023).

  URLBibTeXEndNoteBibSonomy

  Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.

  @misc{hirth2023ordinal,
  abstract = {Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.},
  author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom},
  keywords = {itegpub},
  title = {Ordinal Motifs in Lattices},
  year = 2023
  }
  

  %0 Generic
  %1 hirth2023ordinal
  %A Hirth, Johannes
  %A Horn, Viktoria
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2023
  %T Ordinal Motifs in Lattices
  %U https://arxiv.org/abs/2304.04827
  %X Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.
  

• 1.

  Stubbemann, M., Hanika, T., Schneider, F.M.: Intrinsic Dimension for Large-Scale Geometric Learning. Transactions on Machine Learning Research. (2023).

  URLBibTeXEndNoteBibSonomy

  @article{stubbemann2022intrinsic,
  author = {Stubbemann, Maximilian and Hanika, Tom and Schneider, Friedrich Martin},
  journal = {Transactions on Machine Learning Research},
  keywords = {itegpub},
  title = {Intrinsic Dimension for Large-Scale Geometric Learning},
  year = 2023
  }
  

  %0 Journal Article
  %1 stubbemann2022intrinsic
  %A Stubbemann, Maximilian
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %D 2023
  %J Transactions on Machine Learning Research
  %T Intrinsic Dimension for Large-Scale Geometric Learning
  %U https://openreview.net/forum?id=85BfDdYMBY
  

• 1.

  Felde, M., Koyda, M.: Interval-dismantling for lattices. International Journal of Approximate Reasoning. 159, 108931 (2023). https://doi.org/10.1016/j.ijar.2023.108931.

  URLBibTeXEndNoteDOIBibSonomy

  Dismantling allows for the removal of elements from a poset, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique core with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.

  @article{FELDE2023108931,
  abstract = {Dismantling allows for the removal of elements from a poset, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique core with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.},
  author = {Felde, Maximilian and Koyda, Maren},
  journal = {International Journal of Approximate Reasoning},
  keywords = {Concept_lattice},
  pages = 108931,
  title = {Interval-dismantling for lattices},
  volume = 159,
  year = 2023
  }
  

  %0 Journal Article
  %1 FELDE2023108931
  %A Felde, Maximilian
  %A Koyda, Maren
  %D 2023
  %J International Journal of Approximate Reasoning
  %P 108931
  %R 10.1016/j.ijar.2023.108931
  %T Interval-dismantling for lattices
  %U https://www.sciencedirect.com/science/article/pii/S0888613X23000622
  %V 159
  %X Dismantling allows for the removal of elements from a poset, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique core with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.
  

• 1.

  Koyda, M., Stumme, G.: Factorizing Lattices by Interval Relations. Int. J. Approx. Reason. 157, 70–87 (2023).

  URLBibTeXEndNoteBibSonomy

  @article{koyda2023factorizing,
  author = {Koyda, Maren and Stumme, Gerd},
  journal = {Int. J. Approx. Reason.},
  keywords = {itegpub},
  pages = {70-87},
  title = {Factorizing Lattices by Interval Relations},
  volume = 157,
  year = 2023
  }
  

  %0 Journal Article
  %1 koyda2023factorizing
  %A Koyda, Maren
  %A Stumme, Gerd
  %D 2023
  %J Int. J. Approx. Reason.
  %P 70-87
  %T Factorizing Lattices by Interval Relations
  %U http://dblp.uni-trier.de/db/journals/ijar/ijar157.html#KoydaS23
  %V 157
  

• 1.

  Dürrschnabel, D., Hanika, T., Stumme, G.: Drawing Order Diagrams Through Two-Dimension Extension. Journal of Graph Algorithms and Applications. 27, 783–802 (2023). https://doi.org/10.7155/jgaa.00645.

  URLBibTeXEndNoteDOIBibSonomy

  @article{drrschnabel2023drawing,
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stumme, Gerd},
  journal = {Journal of Graph Algorithms and Applications},
  keywords = {linear_extension},
  number = 9,
  pages = {783–802},
  publisher = {Journal of Graph Algorithms and Applications},
  title = {Drawing Order Diagrams Through Two-Dimension Extension},
  volume = 27,
  year = 2023
  }
  

  %0 Journal Article
  %1 drrschnabel2023drawing
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2023
  %I Journal of Graph Algorithms and Applications
  %J Journal of Graph Algorithms and Applications
  %N 9
  %P 783–802
  %R 10.7155/jgaa.00645
  %T Drawing Order Diagrams Through Two-Dimension Extension
  %U http://dx.doi.org/10.7155/jgaa.00645
  %V 27
  

• 1.

  Dürrschnabel, D.: Explaining and Visualizing Structural Knowledge in Bipartite Graphs, https://kobra.uni-kassel.de/handle/123456789/14847, (2023). https://doi.org/10.17170/KOBRA-202306048157.

  URLBibTeXEndNoteDOIBibSonomy

  @phdthesis{https://doi.org/10.17170/kobra-202306048157,
  author = {Dürrschnabel, Dominik},
  keywords = {itegpub},
  publisher = {Universität Kassel},
  title = {Explaining and Visualizing Structural Knowledge in Bipartite Graphs},
  year = 2023
  }
  

  %0 Thesis
  %1 https://doi.org/10.17170/kobra-202306048157
  %A Dürrschnabel, Dominik
  %D 2023
  %I Universität Kassel
  %R 10.17170/KOBRA-202306048157
  %T Explaining and Visualizing Structural Knowledge in Bipartite Graphs
  %U https://kobra.uni-kassel.de/handle/123456789/14847
  

• 1.

  Stumme, G., Dürrschnabel, D., Hanika, T.: Towards Ordinal Data Science. Transactions on Graph Data and Knowledge. 1, 6:1–6:39 (2023). https://doi.org/10.4230/TGDK.1.1.6.

  URLBibTeXEndNoteDOIBibSonomy

  @article{DBLP:journals/tgdk/StummeDH23,
  author = {Stumme, Gerd and Dürrschnabel, Dominik and Hanika, Tom},
  journal = {Transactions on Graph Data and Knowledge},
  keywords = {itegpub},
  number = 1,
  pages = {6:1--6:39},
  title = {Towards Ordinal Data Science},
  volume = 1,
  year = 2023
  }
  

  %0 Journal Article
  %1 DBLP:journals/tgdk/StummeDH23
  %A Stumme, Gerd
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %D 2023
  %J Transactions on Graph Data and Knowledge
  %N 1
  %P 6:1--6:39
  %R 10.4230/TGDK.1.1.6
  %T Towards Ordinal Data Science
  %U https://doi.org/10.4230/TGDK.1.1.6
  %V 1
  

• 1.

  Hirth, J., Horn, V., Stumme, G., Hanika, T.: Automatic Textual Explanations of Concept Lattices. In: Ojeda{-}Aciego, M., Sauerwald, K., and Jäschke, R. (eds.) Graph-Based Representation and Reasoning - 28th International Conference on Conceptual Structures, {ICCS} 2023, Berlin, Germany, September 11-13, 2023, Proceedings. pp. 138–152 (2023). https://doi.org/doi.org/10.1007/978-3-031-40960-8_12.

  URLBibTeXEndNoteDOIBibSonomy

  Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.

  @inproceedings{hirth2023automatic,
  abstract = {Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.},
  author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom},
  booktitle = {Graph-Based Representation and Reasoning - 28th International Conference on Conceptual Structures, {ICCS} 2023, Berlin, Germany, September 11-13, 2023, Proceedings},
  editor = {Ojeda{-}Aciego, Manuel and Sauerwald, Kai and Jäschke, Robert},
  keywords = {itegpub},
  pages = {138--152},
  title = {Automatic Textual Explanations of Concept Lattices},
  volume = 14133,
  year = 2023
  }
  

  %0 Conference Paper
  %1 hirth2023automatic
  %A Hirth, Johannes
  %A Horn, Viktoria
  %A Stumme, Gerd
  %A Hanika, Tom
  %B Graph-Based Representation and Reasoning - 28th International Conference on Conceptual Structures, {ICCS} 2023, Berlin, Germany, September 11-13, 2023, Proceedings
  %D 2023
  %E Ojeda{-}Aciego, Manuel
  %E Sauerwald, Kai
  %E Jäschke, Robert
  %P 138--152
  %R doi.org/10.1007/978-3-031-40960-8_12
  %T Automatic Textual Explanations of Concept Lattices
  %U http://arxiv.org/abs/2304.08093
  %V 14133
  %X Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.
  

• 1.

  Hanika, T., Hirth, J.: Conceptual Views on Tree Ensemble Classifiers. International Journal of Approximate Reasoning. 108930 (2023). https://doi.org/https://doi.org/10.1016/j.ijar.2023.108930.

  URLBibTeXEndNoteDOIBibSonomy

  Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.

  @article{https://doi.org/10.48550/arxiv.2302.05270,
  abstract = {Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.},
  author = {Hanika, Tom and Hirth, Johannes},
  journal = {International Journal of Approximate Reasoning},
  keywords = {itegpub},
  pages = 108930,
  title = {Conceptual Views on Tree Ensemble Classifiers},
  year = 2023
  }
  

  %0 Journal Article
  %1 https://doi.org/10.48550/arxiv.2302.05270
  %A Hanika, Tom
  %A Hirth, Johannes
  %D 2023
  %J International Journal of Approximate Reasoning
  %P 108930
  %R https://doi.org/10.1016/j.ijar.2023.108930
  %T Conceptual Views on Tree Ensemble Classifiers
  %U https://www.sciencedirect.com/science/article/pii/S0888613X23000610
  %X Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.
  

• 1.

  Ganter, B., Hanika, T., Hirth, J.: Scaling Dimension. In: Dürrschnabel, D. and López-Rodríguez, D. (eds.) Formal Concept Analysis - 17th International Conference, ICFCA 2023, Kassel, Germany, July 17-21, 2023, Proceedings. pp. 64–77. Springer (2023). https://doi.org/10.1007/978-3-031-35949-1_5.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/icfca/GanterHH23,
  author = {Ganter, Bernhard and Hanika, Tom and Hirth, Johannes},
  booktitle = {Formal Concept Analysis - 17th International Conference, ICFCA 2023, Kassel, Germany, July 17-21, 2023, Proceedings},
  editor = {Dürrschnabel, Dominik and López-Rodríguez, Domingo},
  keywords = {itegpub},
  pages = {64--77},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Scaling Dimension},
  volume = 13934,
  year = 2023
  }
  

  %0 Conference Paper
  %1 DBLP:conf/icfca/GanterHH23
  %A Ganter, Bernhard
  %A Hanika, Tom
  %A Hirth, Johannes
  %B Formal Concept Analysis - 17th International Conference, ICFCA 2023, Kassel, Germany, July 17-21, 2023, Proceedings
  %D 2023
  %E Dürrschnabel, Dominik
  %E López-Rodríguez, Domingo
  %I Springer
  %P 64--77
  %R 10.1007/978-3-031-35949-1_5
  %T Scaling Dimension
  %U https://doi.org/10.1007/978-3-031-35949-1_5
  %V 13934
  

• 1.

  Dürrschnabel, D., Stumme, G.: Maximal Ordinal Two-Factorizations. In: Ojeda-Aciego, M., Sauerwald, K., and Jäschke, R. (eds.) Graph-Based Representation and Reasoning. pp. 41–55. Springer Nature Switzerland, Cham (2023).

  BibTeXEndNoteBibSonomy

  Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the incidence relation. In this work, we investigate such ordinal two-factorizations. First, we investigate for formal contexts that omit ordinal two-factorizations the disjointness of the two factors. Then, we show that deciding on the existence of two-factorizations of a given size is an {\$}{\$}{\backslash}textsf{\{}NP{\}}{\$}{\$}NP-complete problem which makes computing maximal factorizations computationally expensive. Finally, we provide the algorithm Ord2Factor that allows us to compute large ordinal two-factorizations.

  @inproceedings{10.1007/978-3-031-40960-8_5,
  abstract = {Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the incidence relation. In this work, we investigate such ordinal two-factorizations. First, we investigate for formal contexts that omit ordinal two-factorizations the disjointness of the two factors. Then, we show that deciding on the existence of two-factorizations of a given size is an {\$}{\$}{\backslash}textsf{\{}NP{\}}{\$}{\$}NP-complete problem which makes computing maximal factorizations computationally expensive. Finally, we provide the algorithm Ord2Factor that allows us to compute large ordinal two-factorizations.},
  address = {Cham},
  author = {Dürrschnabel, Dominik and Stumme, Gerd},
  booktitle = {Graph-Based Representation and Reasoning},
  editor = {Ojeda-Aciego, Manuel and Sauerwald, Kai and Jäschke, Robert},
  keywords = {itegpub},
  pages = {41--55},
  publisher = {Springer Nature Switzerland},
  title = {Maximal Ordinal Two-Factorizations},
  year = 2023
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-40960-8_5
  %A Dürrschnabel, Dominik
  %A Stumme, Gerd
  %B Graph-Based Representation and Reasoning
  %C Cham
  %D 2023
  %E Ojeda-Aciego, Manuel
  %E Sauerwald, Kai
  %E Jäschke, Robert
  %I Springer Nature Switzerland
  %P 41--55
  %T Maximal Ordinal Two-Factorizations
  %X Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the incidence relation. In this work, we investigate such ordinal two-factorizations. First, we investigate for formal contexts that omit ordinal two-factorizations the disjointness of the two factors. Then, we show that deciding on the existence of two-factorizations of a given size is an {\$}{\$}{\backslash}textsf{\{}NP{\}}{\$}{\$}NP-complete problem which makes computing maximal factorizations computationally expensive. Finally, we provide the algorithm Ord2Factor that allows us to compute large ordinal two-factorizations.
  %@ 978-3-031-40960-8
  

• 1.

  Dürrschnabel, D., Stumme, G.: Greedy Discovery of Ordinal Factors, http://arxiv.org/abs/2302.11554, (2023).

  URLBibTeXEndNoteBibSonomy

  In large datasets, it is hard to discover and analyze structure. It is thus common to introduce tags or keywords for the items. In applications, such datasets are then filtered based on these tags. Still, even medium-sized datasets with a few tags result in complex and for humans hard-to-navigate systems. In this work, we adopt the method of ordinal factor analysis to address this problem. An ordinal factor arranges a subset of the tags in a linear order based on their underlying structure. A complete ordinal factorization, which consists of such ordinal factors, precisely represents the original dataset. Based on such an ordinal factorization, we provide a way to discover and explain relationships between different items and attributes in the dataset. However, computing even just one ordinal factor of high cardinality is computationally complex. We thus propose the greedy algorithm in this work. This algorithm extracts ordinal factors using already existing fast algorithms developed in formal concept analysis. Then, we leverage to propose a comprehensive way to discover relationships in the dataset. We furthermore introduce a distance measure based on the representation emerging from the ordinal factorization to discover similar items. To evaluate the method, we conduct a case study on different datasets.

  @misc{durrschnabel2023greedy,
  abstract = {In large datasets, it is hard to discover and analyze structure. It is thus common to introduce tags or keywords for the items. In applications, such datasets are then filtered based on these tags. Still, even medium-sized datasets with a few tags result in complex and for humans hard-to-navigate systems. In this work, we adopt the method of ordinal factor analysis to address this problem. An ordinal factor arranges a subset of the tags in a linear order based on their underlying structure. A complete ordinal factorization, which consists of such ordinal factors, precisely represents the original dataset. Based on such an ordinal factorization, we provide a way to discover and explain relationships between different items and attributes in the dataset. However, computing even just one ordinal factor of high cardinality is computationally complex. We thus propose the greedy algorithm in this work. This algorithm extracts ordinal factors using already existing fast algorithms developed in formal concept analysis. Then, we leverage to propose a comprehensive way to discover relationships in the dataset. We furthermore introduce a distance measure based on the representation emerging from the ordinal factorization to discover similar items. To evaluate the method, we conduct a case study on different datasets.},
  author = {Dürrschnabel, Dominik and Stumme, Gerd},
  keywords = {itegpub},
  note = {cite arxiv:2302.11554Comment: 11 pages, 6 figures, 2 tables, 3 algorithms},
  title = {Greedy Discovery of Ordinal Factors},
  year = 2023
  }
  

  %0 Generic
  %1 durrschnabel2023greedy
  %A Dürrschnabel, Dominik
  %A Stumme, Gerd
  %D 2023
  %T Greedy Discovery of Ordinal Factors
  %U http://arxiv.org/abs/2302.11554
  %X In large datasets, it is hard to discover and analyze structure. It is thus common to introduce tags or keywords for the items. In applications, such datasets are then filtered based on these tags. Still, even medium-sized datasets with a few tags result in complex and for humans hard-to-navigate systems. In this work, we adopt the method of ordinal factor analysis to address this problem. An ordinal factor arranges a subset of the tags in a linear order based on their underlying structure. A complete ordinal factorization, which consists of such ordinal factors, precisely represents the original dataset. Based on such an ordinal factorization, we provide a way to discover and explain relationships between different items and attributes in the dataset. However, computing even just one ordinal factor of high cardinality is computationally complex. We thus propose the greedy algorithm in this work. This algorithm extracts ordinal factors using already existing fast algorithms developed in formal concept analysis. Then, we leverage to propose a comprehensive way to discover relationships in the dataset. We furthermore introduce a distance measure based on the representation emerging from the ordinal factorization to discover similar items. To evaluate the method, we conduct a case study on different datasets.
  

• 1.

  Schäfermeier, B., Hirth, J., Hanika, T.: Research Topic Flows in Co-Authorship Networks. Scientometrics. 128, 5051–5078 (2023). https://doi.org/10.1007/s11192-022-04529-w.

  BibTeXEndNoteDOIBibSonomy

  In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

  @article{schafermeier2022research,
  abstract = {In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.},
  author = {Schäfermeier, Bastian and Hirth, Johannes and Hanika, Tom},
  journal = {Scientometrics},
  keywords = {co-authorships},
  month = {09},
  number = 9,
  pages = {5051--5078},
  title = {Research Topic Flows in Co-Authorship Networks},
  volume = 128,
  year = 2023
  }
  

  %0 Journal Article
  %1 schafermeier2022research
  %A Schäfermeier, Bastian
  %A Hirth, Johannes
  %A Hanika, Tom
  %D 2023
  %J Scientometrics
  %N 9
  %P 5051--5078
  %R 10.1007/s11192-022-04529-w
  %T Research Topic Flows in Co-Authorship Networks
  %V 128
  %X In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.
  

2022

• 1.

  Dürrschnabel, D., Hanika, T., Stubbemann, M.: FCA2VEC: Embedding Techniques for Formal Concept Analysis. In: Missaoui, R., Kwuida, L., and Abdessalem, T. (eds.) Complex Data Analytics with Formal Concept Analysis. pp. 47–74. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-93278-7_3.

  URLBibTeXEndNoteDOIBibSonomy

  Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding `latent semantic analysis' recent approaches like `word2vec' or `node2vec' are well established tools in this realm. In the present paper we add to this line of research by introducing `fca2vec', a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computationally feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA community.

  @inbook{Dürrschnabel2022,
  abstract = {Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding `latent semantic analysis' recent approaches like `word2vec' or `node2vec' are well established tools in this realm. In the present paper we add to this line of research by introducing `fca2vec', a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computationally feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA community.},
  address = {Cham},
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stubbemann, Maximilian},
  booktitle = {Complex Data Analytics with Formal Concept Analysis},
  editor = {Missaoui, Rokia and Kwuida, L{é}onard and Abdessalem, Talel},
  keywords = {fca2vec},
  pages = {47--74},
  publisher = {Springer International Publishing},
  title = {FCA2VEC: Embedding Techniques for Formal Concept Analysis},
  year = 2022
  }
  

  %0 Book Section
  %1 Dürrschnabel2022
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stubbemann, Maximilian
  %B Complex Data Analytics with Formal Concept Analysis
  %C Cham
  %D 2022
  %E Missaoui, Rokia
  %E Kwuida, L{é}onard
  %E Abdessalem, Talel
  %I Springer International Publishing
  %P 47--74
  %R 10.1007/978-3-030-93278-7_3
  %T FCA2VEC: Embedding Techniques for Formal Concept Analysis
  %U https://doi.org/10.1007/978-3-030-93278-7_3
  %X Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding `latent semantic analysis' recent approaches like `word2vec' or `node2vec' are well established tools in this realm. In the present paper we add to this line of research by introducing `fca2vec', a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computationally feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA community.
  %@ 978-3-030-93278-7
  

• 1.

  Stubbemann, M., Hanika, T., Schneider, F.M.: Intrinsic Dimension for Large-Scale Geometric Learning, https://arxiv.org/abs/2210.05301, (2022).

  URLBibTeXEndNoteBibSonomy

  @misc{stubbemann2022intrinsic,
  author = {Stubbemann, Maximilian and Hanika, Tom and Schneider, Friedrich Martin},
  keywords = {outdated},
  title = {Intrinsic Dimension for Large-Scale Geometric Learning},
  year = 2022
  }
  

  %0 Generic
  %1 stubbemann2022intrinsic
  %A Stubbemann, Maximilian
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %D 2022
  %T Intrinsic Dimension for Large-Scale Geometric Learning
  %U https://arxiv.org/abs/2210.05301
  

• 1.

  Hanika, T., Hirth, J.: On the lattice of conceptual measurements. Information Sciences. 613, 453–468 (2022). https://doi.org/https://doi.org/10.1016/j.ins.2022.09.005.

  URLBibTeXEndNoteDOIBibSonomy

  We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.

  @article{hanika2020lattice,
  abstract = {We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.},
  author = {Hanika, Tom and Hirth, Johannes},
  journal = {Information Sciences},
  keywords = {selected},
  pages = {453-468},
  title = {On the lattice of conceptual measurements},
  volume = 613,
  year = 2022
  }
  

  %0 Journal Article
  %1 hanika2020lattice
  %A Hanika, Tom
  %A Hirth, Johannes
  %D 2022
  %J Information Sciences
  %P 453-468
  %R https://doi.org/10.1016/j.ins.2022.09.005
  %T On the lattice of conceptual measurements
  %U https://www.sciencedirect.com/science/article/pii/S0020025522010489
  %V 613
  %X We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.
  

• 1.

  Hanika, T., Schneider, F.M., Stumme, G.: {Intrinsic dimension of geometric data sets}. Tohoku Mathematical Journal. 74, 23–52 (2022). https://doi.org/10.2748/tmj.20201015a.

  URLBibTeXEndNoteDOIBibSonomy

  The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.

  @article{10.2748/tmj.20201015a,
  abstract = {The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.},
  author = {Hanika, Tom and Schneider, Friedrich Martin and Stumme, Gerd},
  journal = {Tohoku Mathematical Journal},
  keywords = {itegpub},
  number = 1,
  pages = {23 -- 52},
  publisher = {Tohoku University, Mathematical Institute},
  title = {{Intrinsic dimension of geometric data sets}},
  volume = 74,
  year = 2022
  }
  

  %0 Journal Article
  %1 10.2748/tmj.20201015a
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %A Stumme, Gerd
  %D 2022
  %I Tohoku University, Mathematical Institute
  %J Tohoku Mathematical Journal
  %N 1
  %P 23 -- 52
  %R 10.2748/tmj.20201015a
  %T {Intrinsic dimension of geometric data sets}
  %U https://doi.org/10.2748/tmj.20201015a
  %V 74
  %X The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
  

• 1.

  Dürrschnabel, D., Hanika, T., Stumme, G.: Discovering Locally Maximal Bipartite Subgraphs, http://arxiv.org/abs/2211.10446, (2022).

  URLBibTeXEndNoteBibSonomy

  Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs. Yet, discovering them in large graphs is known to be computationally hard. Therefore, we consider in this work a weaker notion of this problem, where we discard the maximality constraint in favor of inclusion maximality. Thus, we aim to discover locally maximal bipartite subgraphs. For this, we present three heuristic approaches to extract such subgraphs and compare their results to the solutions of the global problem. For the latter, we employ the algorithmic strength of fast SAT-solvers. Our three proposed heuristics are based on a greedy strategy, a simulated annealing approach, and a genetic algorithm, respectively. We evaluate all four algorithms with respect to their time requirement and the vertex cardinality of the discovered bipartite subgraphs on several benchmark datasets

  @misc{durrschnabel2022discovering,
  abstract = {Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs. Yet, discovering them in large graphs is known to be computationally hard. Therefore, we consider in this work a weaker notion of this problem, where we discard the maximality constraint in favor of inclusion maximality. Thus, we aim to discover locally maximal bipartite subgraphs. For this, we present three heuristic approaches to extract such subgraphs and compare their results to the solutions of the global problem. For the latter, we employ the algorithmic strength of fast SAT-solvers. Our three proposed heuristics are based on a greedy strategy, a simulated annealing approach, and a genetic algorithm, respectively. We evaluate all four algorithms with respect to their time requirement and the vertex cardinality of the discovered bipartite subgraphs on several benchmark datasets},
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stumme, Gerd},
  keywords = {itegpub},
  note = {cite arxiv:2211.10446Comment: 12 pages, 3 figures, 3 tables},
  title = {Discovering Locally Maximal Bipartite Subgraphs},
  year = 2022
  }
  

  %0 Generic
  %1 durrschnabel2022discovering
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2022
  %T Discovering Locally Maximal Bipartite Subgraphs
  %U http://arxiv.org/abs/2211.10446
  %X Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs. Yet, discovering them in large graphs is known to be computationally hard. Therefore, we consider in this work a weaker notion of this problem, where we discard the maximality constraint in favor of inclusion maximality. Thus, we aim to discover locally maximal bipartite subgraphs. For this, we present three heuristic approaches to extract such subgraphs and compare their results to the solutions of the global problem. For the latter, we employ the algorithmic strength of fast SAT-solvers. Our three proposed heuristics are based on a greedy strategy, a simulated annealing approach, and a genetic algorithm, respectively. We evaluate all four algorithms with respect to their time requirement and the vertex cardinality of the discovered bipartite subgraphs on several benchmark datasets
  

• 1.

  Felde, M., Stumme, G.: Attribute Exploration with Multiple Contradicting Partial Experts. In: Braun, T., Cristea, D., and J{ä}schke, R. (eds.) Graph-Based Representation and Reasoning. pp. 51–65. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-16663-1_5.

  BibTeXEndNoteDOIBibSonomy

  Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their shared views. Each expert has their own view of the domain and the views of multiple experts may contain contradicting information.

  @inproceedings{10.1007/978-3-031-16663-1_5,
  abstract = {Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their shared views. Each expert has their own view of the domain and the views of multiple experts may contain contradicting information.},
  address = {Cham},
  author = {Felde, Maximilian and Stumme, Gerd},
  booktitle = {Graph-Based Representation and Reasoning},
  editor = {Braun, Tanya and Cristea, Diana and J{ä}schke, Robert},
  keywords = {attribute-exploration},
  pages = {51--65},
  publisher = {Springer International Publishing},
  title = {Attribute Exploration with Multiple Contradicting Partial Experts},
  year = 2022
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-16663-1_5
  %A Felde, Maximilian
  %A Stumme, Gerd
  %B Graph-Based Representation and Reasoning
  %C Cham
  %D 2022
  %E Braun, Tanya
  %E Cristea, Diana
  %E J{ä}schke, Robert
  %I Springer International Publishing
  %P 51--65
  %R 10.1007/978-3-031-16663-1_5
  %T Attribute Exploration with Multiple Contradicting Partial Experts
  %X Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their shared views. Each expert has their own view of the domain and the views of multiple experts may contain contradicting information.
  %@ 978-3-031-16663-1
  

• 1.

  Felde, M., Koyda, M.: Interval-Dismantling for Lattices, https://arxiv.org/abs/2208.01479, (2022). https://doi.org/10.48550/arXiv.2208.01479.

  URLBibTeXEndNoteDOIBibSonomy

  Dismantling allows for the removal of elements of a set, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique kernel with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.

  @preprint{felde2022intervaldismantling,
  abstract = {Dismantling allows for the removal of elements of a set, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique kernel with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.},
  author = {Felde, Maximilian and Koyda, Maren},
  keywords = {myown},
  note = {cite arxiv:2208.01479Comment: 12 pages, 5 figures, 1 algorithm},
  title = {Interval-Dismantling for Lattices},
  year = 2022
  }
  

  %0 Generic
  %1 felde2022intervaldismantling
  %A Felde, Maximilian
  %A Koyda, Maren
  %D 2022
  %R 10.48550/arXiv.2208.01479
  %T Interval-Dismantling for Lattices
  %U https://arxiv.org/abs/2208.01479
  %X Dismantling allows for the removal of elements of a set, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique kernel with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.
  

• 1.

  Schäfermeier, B., Hirth, J., Hanika, T.: Research Topic Flows in Co-Authorship Networks, https://doi.org/10.1007/s11192-022-04529-w, (2022). https://doi.org/10.1007/s11192-022-04529-w.

  URLBibTeXEndNoteDOIBibSonomy

  In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

  @misc{schafermeier2022research,
  abstract = {In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.},
  author = {Schäfermeier, Bastian and Hirth, Johannes and Hanika, Tom},
  journal = {Scientometrics},
  keywords = {co-authorships},
  month = 10,
  title = {Research Topic Flows in Co-Authorship Networks},
  year = 2022
  }
  

  %0 Generic
  %1 schafermeier2022research
  %A Schäfermeier, Bastian
  %A Hirth, Johannes
  %A Hanika, Tom
  %D 2022
  %J Scientometrics
  %R 10.1007/s11192-022-04529-w
  %T Research Topic Flows in Co-Authorship Networks
  %U https://doi.org/10.1007/s11192-022-04529-w
  %X In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.
  

• 1.

  Stubbemann, M., Stumme, G.: LG4AV: Combining Language Models and Graph Neural Networks for Author Verification. In: Bouadi, T., Fromont, E., and H{ü}llermeier, E. (eds.) Advances in Intelligent Data Analysis XX. pp. 315–326. Springer International Publishing, Cham (2022).

  URLBibTeXEndNoteBibSonomy

  The verification of document authorships is important in various settings. Researchers are for example judged and compared by the amount and impact of their publications and public figures are confronted by their posts on social media. Therefore, it is important that authorship information in frequently used data sets is correct. The question whether a given document is written by a given author is commonly referred to as authorship verification (AV). While AV is a widely investigated problem in general, only few works consider settings where the documents are short and written in a rather uniform style. This makes most approaches impractical for bibliometric data. Here, authorships of scientific publications have to be verified, often with just abstracts and titles available. To this point, we present LG4AV which combines language models and graph neural networks for authorship verification. By directly feeding the available texts in a pre-trained transformer architecture, our model does not need any hand-crafted stylometric features that are not meaningful in scenarios where the writing style is, at least to some extent, standardized. By the incorporation of a graph neural network structure, our model can benefit from relations between authors that are meaningful with respect to the verification process.

  @inproceedings{10.1007/978-3-031-01333-1_25,
  abstract = {The verification of document authorships is important in various settings. Researchers are for example judged and compared by the amount and impact of their publications and public figures are confronted by their posts on social media. Therefore, it is important that authorship information in frequently used data sets is correct. The question whether a given document is written by a given author is commonly referred to as authorship verification (AV). While AV is a widely investigated problem in general, only few works consider settings where the documents are short and written in a rather uniform style. This makes most approaches impractical for bibliometric data. Here, authorships of scientific publications have to be verified, often with just abstracts and titles available. To this point, we present LG4AV which combines language models and graph neural networks for authorship verification. By directly feeding the available texts in a pre-trained transformer architecture, our model does not need any hand-crafted stylometric features that are not meaningful in scenarios where the writing style is, at least to some extent, standardized. By the incorporation of a graph neural network structure, our model can benefit from relations between authors that are meaningful with respect to the verification process.},
  address = {Cham},
  author = {Stubbemann, Maximilian and Stumme, Gerd},
  booktitle = {Advances in Intelligent Data Analysis XX},
  editor = {Bouadi, Tassadit and Fromont, Elisa and H{ü}llermeier, Eyke},
  keywords = {itegpub},
  pages = {315--326},
  publisher = {Springer International Publishing},
  title = {LG4AV: Combining Language Models and Graph Neural Networks for Author Verification},
  year = 2022
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-01333-1_25
  %A Stubbemann, Maximilian
  %A Stumme, Gerd
  %B Advances in Intelligent Data Analysis XX
  %C Cham
  %D 2022
  %E Bouadi, Tassadit
  %E Fromont, Elisa
  %E H{ü}llermeier, Eyke
  %I Springer International Publishing
  %P 315--326
  %T LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
  %U https://link.springer.com/chapter/10.1007/978-3-031-01333-1_25
  %X The verification of document authorships is important in various settings. Researchers are for example judged and compared by the amount and impact of their publications and public figures are confronted by their posts on social media. Therefore, it is important that authorship information in frequently used data sets is correct. The question whether a given document is written by a given author is commonly referred to as authorship verification (AV). While AV is a widely investigated problem in general, only few works consider settings where the documents are short and written in a rather uniform style. This makes most approaches impractical for bibliometric data. Here, authorships of scientific publications have to be verified, often with just abstracts and titles available. To this point, we present LG4AV which combines language models and graph neural networks for authorship verification. By directly feeding the available texts in a pre-trained transformer architecture, our model does not need any hand-crafted stylometric features that are not meaningful in scenarios where the writing style is, at least to some extent, standardized. By the incorporation of a graph neural network structure, our model can benefit from relations between authors that are meaningful with respect to the verification process.
  %@ 978-3-031-01333-1
  

• 1.

  D{{ü}}rrschnabel, D., Hanika, T., Stubbemann, M.: {FCA2VEC:} Embedding Techniques for Formal Concept Analysis. In: Missaoui, R., Kwuida, L., and Abdessalem, T. (eds.) Complex Data Analytics with Formal Concept Analysis. pp. 47–74. Springer International Publishing (2022). https://doi.org/10.1007/978-3-030-93278-7_3.

  URLBibTeXEndNoteDOIBibSonomy

  @incollection{DBLP:books/sp/missaoui2022/DurrschnabelHS22,
  author = {D{{ü}}rrschnabel, Dominik and Hanika, Tom and Stubbemann, Maximilian},
  booktitle = {Complex Data Analytics with Formal Concept Analysis},
  editor = {Missaoui, Rokia and Kwuida, L{{é}}onard and Abdessalem, Talel},
  keywords = {itegpub},
  pages = {47--74},
  publisher = {Springer International Publishing},
  title = {{FCA2VEC:} Embedding Techniques for Formal Concept Analysis},
  year = 2022
  }
  

  %0 Book Section
  %1 DBLP:books/sp/missaoui2022/DurrschnabelHS22
  %A D{{ü}}rrschnabel, Dominik
  %A Hanika, Tom
  %A Stubbemann, Maximilian
  %B Complex Data Analytics with Formal Concept Analysis
  %D 2022
  %E Missaoui, Rokia
  %E Kwuida, L{{é}}onard
  %E Abdessalem, Talel
  %I Springer International Publishing
  %P 47--74
  %R 10.1007/978-3-030-93278-7_3
  %T {FCA2VEC:} Embedding Techniques for Formal Concept Analysis
  %U https://doi.org/10.1007/978-3-030-93278-7_3
  

• 1.

  Schäfermeier, B., Stumme, G., Hanika, T.: Mapping Research Trajectories, https://arxiv.org/abs/2204.11859, (2022). https://doi.org/10.48550/ARXIV.2204.11859.

  URLBibTeXEndNoteDOIBibSonomy

  @misc{https://doi.org/10.48550/arxiv.2204.11859,
  author = {Schäfermeier, Bastian and Stumme, Gerd and Hanika, Tom},
  keywords = {trajectory_mapping},
  publisher = {arXiv},
  title = {Mapping Research Trajectories},
  year = 2022
  }
  

  %0 Generic
  %1 https://doi.org/10.48550/arxiv.2204.11859
  %A Schäfermeier, Bastian
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2022
  %I arXiv
  %R 10.48550/ARXIV.2204.11859
  %T Mapping Research Trajectories
  %U https://arxiv.org/abs/2204.11859
  

• 1.

  Hanika, T., Hirth, J.: Knowledge cores in large formal contexts. Annals of Mathematics and Artificial Intelligence. 90, 537–567 (2022). https://doi.org/10.1007/s10472-022-09790-6.

  URLBibTeXEndNoteDOIBibSonomy

  Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.

  @article{Hanika2022,
  abstract = {Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.},
  author = {Hanika, Tom and Hirth, Johannes},
  journal = {Annals of Mathematics and Artificial Intelligence},
  keywords = {itegpub},
  month = {04},
  number = 6,
  pages = {537--567},
  title = {Knowledge cores in large formal contexts},
  volume = 90,
  year = 2022
  }
  

  %0 Journal Article
  %1 Hanika2022
  %A Hanika, Tom
  %A Hirth, Johannes
  %D 2022
  %J Annals of Mathematics and Artificial Intelligence
  %N 6
  %P 537--567
  %R 10.1007/s10472-022-09790-6
  %T Knowledge cores in large formal contexts
  %U https://doi.org/10.1007/s10472-022-09790-6
  %V 90
  %X Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.
  

• 1.

  Hirth, J., Hanika, T.: Formal Conceptual Views in Neural Networks, http://arxiv.org/abs/2209.13517, (2022).

  URLBibTeXEndNoteBibSonomy

  Explaining neural network models is a challenging task that remains unsolved in its entirety to this day. This is especially true for high dimensional and complex data. With the present work, we introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. Both provide novel analysis methods to enable a human AI analyst to grasp deeper insights into the knowledge that is captured by the neurons of a network. We test the conceptual expressivity of our novel views through different experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to which extent the views allow to quantify the conceptual similarity of different learning architectures. Finally, we demonstrate how conceptual views can be applied for abductive learning of human comprehensible rules from neurons. In summary, with our work, we contribute to the most relevant task of globally explaining neural networks models.

  @misc{hirth2022formal,
  abstract = {Explaining neural network models is a challenging task that remains unsolved in its entirety to this day. This is especially true for high dimensional and complex data. With the present work, we introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. Both provide novel analysis methods to enable a human AI analyst to grasp deeper insights into the knowledge that is captured by the neurons of a network. We test the conceptual expressivity of our novel views through different experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to which extent the views allow to quantify the conceptual similarity of different learning architectures. Finally, we demonstrate how conceptual views can be applied for abductive learning of human comprehensible rules from neurons. In summary, with our work, we contribute to the most relevant task of globally explaining neural networks models.},
  author = {Hirth, Johannes and Hanika, Tom},
  keywords = {NN},
  note = {cite arxiv:2209.13517Comment: 17 pages, 8 figures, 9 tables},
  title = {Formal Conceptual Views in Neural Networks},
  year = 2022
  }
  

  %0 Generic
  %1 hirth2022formal
  %A Hirth, Johannes
  %A Hanika, Tom
  %D 2022
  %T Formal Conceptual Views in Neural Networks
  %U http://arxiv.org/abs/2209.13517
  %X Explaining neural network models is a challenging task that remains unsolved in its entirety to this day. This is especially true for high dimensional and complex data. With the present work, we introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. Both provide novel analysis methods to enable a human AI analyst to grasp deeper insights into the knowledge that is captured by the neurons of a network. We test the conceptual expressivity of our novel views through different experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to which extent the views allow to quantify the conceptual similarity of different learning architectures. Finally, we demonstrate how conceptual views can be applied for abductive learning of human comprehensible rules from neurons. In summary, with our work, we contribute to the most relevant task of globally explaining neural networks models.
  

2021

• 1.

  Hanika, T., Hirth, J.: Exploring Scale-Measures of Data Sets. In: Braud, A., Buzmakov, A., Hanika, T., and Ber, F.L. (eds.) Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings. pp. 261–269. Springer (2021). https://doi.org/10.1007/978-3-030-77867-5_17.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/icfca/HanikaH21,
  author = {Hanika, Tom and Hirth, Johannes},
  booktitle = {Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings},
  editor = {Braud, Agn{{è}}s and Buzmakov, Aleksey and Hanika, Tom and Ber, Florence Le},
  keywords = {itegpub},
  pages = {261--269},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Exploring Scale-Measures of Data Sets},
  volume = 12733,
  year = 2021
  }
  

  %0 Conference Paper
  %1 DBLP:conf/icfca/HanikaH21
  %A Hanika, Tom
  %A Hirth, Johannes
  %B Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings
  %D 2021
  %E Braud, Agn{{è}}s
  %E Buzmakov, Aleksey
  %E Hanika, Tom
  %E Ber, Florence Le
  %I Springer
  %P 261--269
  %R 10.1007/978-3-030-77867-5_17
  %T Exploring Scale-Measures of Data Sets
  %U https://doi.org/10.1007/978-3-030-77867-5_17
  %V 12733
  

• 1.

  Braun, T., Gehrke, M., Hanika, T., Hernandez, N. eds.: Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings. Springer (2021). https://doi.org/10.1007/978-3-030-86982-3.

  URLBibTeXEndNoteDOIBibSonomy

  @proceedings{DBLP:conf/iccs/2021,
  editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},
  keywords = {itegpub},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings},
  volume = 12879,
  year = 2021
  }
  

  %0 Conference Proceedings
  %1 DBLP:conf/iccs/2021
  %B Lecture Notes in Computer Science
  %D 2021
  %E Braun, Tanya
  %E Gehrke, Marcel
  %E Hanika, Tom
  %E Hernandez, Nathalie
  %I Springer
  %R 10.1007/978-3-030-86982-3
  %T Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings
  %U https://doi.org/10.1007/978-3-030-86982-3
  %V 12879
  %@ 978-3-030-86981-6
  

• 1.

  Draude, C., Gruhl, C., Hornung, G., Kropf, J., Lamla, J., Leimeister, J.M., Sick, B., Stumme, G.: Social Machines. Informatik Spektrum. (2021). https://doi.org/10.1007/s00287-021-01421-4.

  URLBibTeXEndNoteDOIBibSonomy

  @article{2021,
  author = {Draude, Claude and Gruhl, Christian and Hornung, Gerrit and Kropf, Jonathan and Lamla, Jörn and Leimeister, Jan Marco and Sick, Bernhard and Stumme, Gerd},
  journal = {Informatik Spektrum},
  keywords = {itegpub},
  month = 11,
  title = {Social Machines},
  year = 2021
  }
  

  %0 Journal Article
  %1 2021
  %A Draude, Claude
  %A Gruhl, Christian
  %A Hornung, Gerrit
  %A Kropf, Jonathan
  %A Lamla, Jörn
  %A Leimeister, Jan Marco
  %A Sick, Bernhard
  %A Stumme, Gerd
  %D 2021
  %J Informatik Spektrum
  %R 10.1007/s00287-021-01421-4
  %T Social Machines
  %U https://doi.org/10.1007%2Fs00287-021-01421-4
  

• 1.

  Schaefermeier, B., Stumme, G., Hanika, T.: Topic space trajectories. Scientometrics. 126, 5759–5795 (2021). https://doi.org/10.1007/s11192-021-03931-0.

  URLBibTeXEndNoteDOIBibSonomy

  The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.

  @article{schafermeier2020topic,
  abstract = {The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.},
  author = {Schaefermeier, Bastian and Stumme, Gerd and Hanika, Tom},
  journal = {Scientometrics},
  keywords = 2021,
  month = {07},
  number = 7,
  pages = {5759-5795},
  publisher = {Springer},
  title = {Topic space trajectories},
  volume = 126,
  year = 2021
  }
  

  %0 Journal Article
  %1 schafermeier2020topic
  %A Schaefermeier, Bastian
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2021
  %I Springer
  %J Scientometrics
  %N 7
  %P 5759-5795
  %R 10.1007/s11192-021-03931-0
  %T Topic space trajectories
  %U https://doi.org/10.1007/s11192-021-03931-0
  %V 126
  %X The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
  

• 1.

  Stubbemann, L., Dürrschnabel, D., Refflinghaus, R.: Neural Networks for Semantic Gaze Analysis in XR Settings. In: Bulling, A., Huckauf, A., Gellersen, H., Weiskopf, D., Bace, M., Hirzle, T., Alt, F., Pfeiffer, T., Bednarik, R., Krejtz, K., Blascheck, T., Burch, M., Kiefer, P., Dodd, M.D., and Sharif, B. (eds.) ETRA ’21 Full Papers: ACM Symposium on Eye Tracking Research and Applications. {ACM} (2021). https://doi.org/10.1145/3448017.3457380.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{Stubbemann_2021,
  author = {Stubbemann, Lena and Dürrschnabel, Dominik and Refflinghaus, Robert},
  booktitle = {ETRA '21 Full Papers: ACM Symposium on Eye Tracking Research and Applications},
  editor = {Bulling, Andreas and Huckauf, Anke and Gellersen, Hans and Weiskopf, Daniel and Bace, Mihai and Hirzle, Teresa and Alt, Florian and Pfeiffer, Thies and Bednarik, Roman and Krejtz, Krzysztof and Blascheck, Tanja and Burch, Michael and Kiefer, Peter and Dodd, Michael D. and Sharif, Bonita},
  keywords = {itegpub},
  month = {05},
  publisher = {{ACM}},
  title = {Neural Networks for Semantic Gaze Analysis in XR Settings},
  year = 2021
  }
  

  %0 Conference Paper
  %1 Stubbemann_2021
  %A Stubbemann, Lena
  %A Dürrschnabel, Dominik
  %A Refflinghaus, Robert
  %B ETRA '21 Full Papers: ACM Symposium on Eye Tracking Research and Applications
  %D 2021
  %E Bulling, Andreas
  %E Huckauf, Anke
  %E Gellersen, Hans
  %E Weiskopf, Daniel
  %E Bace, Mihai
  %E Hirzle, Teresa
  %E Alt, Florian
  %E Pfeiffer, Thies
  %E Bednarik, Roman
  %E Krejtz, Krzysztof
  %E Blascheck, Tanja
  %E Burch, Michael
  %E Kiefer, Peter
  %E Dodd, Michael D.
  %E Sharif, Bonita
  %I {ACM}
  %R 10.1145/3448017.3457380
  %T Neural Networks for Semantic Gaze Analysis in XR Settings
  %U https://doi.org/10.1145%2F3448017.3457380
  %@ 978-1-4503-8344-8
  

• 1.

  Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jäschke, R., Stumme, G.: Proximity Dimensions and the Emergence of Collaboration: A HypTrails Study on German AI Research. Scientometrics. 126, 9847–9868 (2021). https://doi.org/https://doi.org/10.1007/s11192-021-03922-1.

  URLBibTeXEndNoteDOIBibSonomy

  @article{koopmann2021proximity,
  author = {Koopmann, T. and Stubbemann, M. and Kapa, M. and Paris, M. and Buenstorf, G. and Hanika, T. and Hotho, A. and Jäschke, R. and Stumme, G.},
  journal = {Scientometrics},
  keywords = {itegpub},
  month = {03},
  number = 12,
  pages = {9847–9868},
  title = {Proximity Dimensions and the Emergence of Collaboration: A HypTrails Study on German AI Research},
  volume = 126,
  year = 2021
  }
  

  %0 Journal Article
  %1 koopmann2021proximity
  %A Koopmann, T.
  %A Stubbemann, M.
  %A Kapa, M.
  %A Paris, M.
  %A Buenstorf, G.
  %A Hanika, T.
  %A Hotho, A.
  %A Jäschke, R.
  %A Stumme, G.
  %D 2021
  %J Scientometrics
  %N 12
  %P 9847–9868
  %R https://doi.org/10.1007/s11192-021-03922-1
  %T Proximity Dimensions and the Emergence of Collaboration: A HypTrails Study on German AI Research
  %U https://link.springer.com/article/10.1007/s11192-021-03922-1
  %V 126
  

• 1.

  D{ü}rrschnabel, D., Stumme, G.: Force-Directed Layout of Order Diagrams Using Dimensional Reduction. In: Braud, A., Buzmakov, A., Hanika, T., and Le Ber, F. (eds.) Formal Concept Analysis. pp. 224–240. Springer International Publishing, Cham (2021).

  BibTeXEndNoteBibSonomy

  Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.

  @inproceedings{10.1007/978-3-030-77867-5_14,
  abstract = {Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.},
  address = {Cham},
  author = {D{ü}rrschnabel, Dominik and Stumme, Gerd},
  booktitle = {Formal Concept Analysis},
  editor = {Braud, Agn{è}s and Buzmakov, Aleksey and Hanika, Tom and Le Ber, Florence},
  keywords = {itegpub},
  pages = {224--240},
  publisher = {Springer International Publishing},
  title = {Force-Directed Layout of Order Diagrams Using Dimensional Reduction},
  year = 2021
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-030-77867-5_14
  %A D{ü}rrschnabel, Dominik
  %A Stumme, Gerd
  %B Formal Concept Analysis
  %C Cham
  %D 2021
  %E Braud, Agn{è}s
  %E Buzmakov, Aleksey
  %E Hanika, Tom
  %E Le Ber, Florence
  %I Springer International Publishing
  %P 224--240
  %T Force-Directed Layout of Order Diagrams Using Dimensional Reduction
  %X Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.
  %@ 978-3-030-77867-5
  

• 1.

  Stubbemann, M., Stumme, G.: The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks. arXiv preprint arXiv:2110.13774. (2021).

  BibTeXEndNoteBibSonomy

  @article{stubbemann2021mont,
  author = {Stubbemann, Maximilian and Stumme, Gerd},
  journal = {arXiv preprint arXiv:2110.13774},
  keywords = {itegpub},
  title = {The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks},
  year = 2021
  }
  

  %0 Journal Article
  %1 stubbemann2021mont
  %A Stubbemann, Maximilian
  %A Stumme, Gerd
  %D 2021
  %J arXiv preprint arXiv:2110.13774
  %T The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks
  

• 1.

  Schaefermeier, B., Stumme, G., Hanika, T.: Topological Indoor Mapping through WiFi Signals. (2021).

  URLBibTeXEndNoteBibSonomy

  The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.

  @article{schaefermeier2021topological,
  abstract = {The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.},
  author = {Schaefermeier, Bastian and Stumme, Gerd and Hanika, Tom},
  keywords = {itegpub},
  note = {cite arxiv:2106.09789Comment: 18 pages},
  title = {Topological Indoor Mapping through WiFi Signals},
  year = 2021
  }
  

  %0 Journal Article
  %1 schaefermeier2021topological
  %A Schaefermeier, Bastian
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2021
  %T Topological Indoor Mapping through WiFi Signals
  %U http://arxiv.org/abs/2106.09789
  %X The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.
  

• 1.

  Koyda, M., Stumme, G.: Boolean Substructures in Formal Concept Analysis. In: ICFCA: International Conference on Formal Concept Analysis. pp. 38–53. Springer (2021).

  BibTeXEndNoteBibSonomy

  @conference{koyda2021boolean,
  author = {Koyda, Maren and Stumme, Gerd},
  booktitle = {Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings},
  journal = {ICFCA: International Conference on Formal Concept Analysis},
  keywords = {itegpub},
  pages = {38-53},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Boolean Substructures in Formal Concept Analysis},
  year = 2021
  }
  

  %0 Generic
  %1 koyda2021boolean
  %A Koyda, Maren
  %A Stumme, Gerd
  %B Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings
  %D 2021
  %I Springer
  %J ICFCA: International Conference on Formal Concept Analysis
  %P 38-53
  %T Boolean Substructures in Formal Concept Analysis
  %@ 978-3-030-77866-8
  

• 1.

  Dürrschnabel, D., Koyda, M., Stumme, G.: Attribute Selection Using Contranominal Scales. In: Braun, T., Gehrke, M., Hanika, T., and Hernandez, N. (eds.) Graph-Based Representation and Reasoning. pp. 127–141. Springer International Publishing, Cham (2021).

  BibTeXEndNoteBibSonomy

  Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.

  @inproceedings{10.1007/978-3-030-86982-3_10,
  abstract = {Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.},
  address = {Cham},
  author = {Dürrschnabel, Dominik and Koyda, Maren and Stumme, Gerd},
  booktitle = {Graph-Based Representation and Reasoning},
  editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},
  keywords = {itegpub},
  pages = {127--141},
  publisher = {Springer International Publishing},
  title = {Attribute Selection Using Contranominal Scales},
  year = 2021
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-030-86982-3_10
  %A Dürrschnabel, Dominik
  %A Koyda, Maren
  %A Stumme, Gerd
  %B Graph-Based Representation and Reasoning
  %C Cham
  %D 2021
  %E Braun, Tanya
  %E Gehrke, Marcel
  %E Hanika, Tom
  %E Hernandez, Nathalie
  %I Springer International Publishing
  %P 127--141
  %T Attribute Selection Using Contranominal Scales
  %X Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.
  %@ 978-3-030-86982-3
  

• 1.

  Felde, M., Stumme, G.: Triadic Exploration and Exploration with Multiple Experts. In: Braud, A., Buzmakov, A., Hanika, T., and Le Ber, F. (eds.) Formal Concept Analysis. pp. 175–191. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-77867-5_11.

  BibTeXEndNoteDOIBibSonomy

  Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.

  @inproceedings{10.1007/978-3-030-77867-5_11,
  abstract = {Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.},
  address = {Cham},
  author = {Felde, Maximilian and Stumme, Gerd},
  booktitle = {Formal Concept Analysis},
  editor = {Braud, Agn{è}s and Buzmakov, Aleksey and Hanika, Tom and Le Ber, Florence},
  keywords = 2021,
  pages = {175--191},
  publisher = {Springer International Publishing},
  title = {Triadic Exploration and Exploration with Multiple Experts},
  year = 2021
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-030-77867-5_11
  %A Felde, Maximilian
  %A Stumme, Gerd
  %B Formal Concept Analysis
  %C Cham
  %D 2021
  %E Braud, Agn{è}s
  %E Buzmakov, Aleksey
  %E Hanika, Tom
  %E Le Ber, Florence
  %I Springer International Publishing
  %P 175--191
  %R 10.1007/978-3-030-77867-5_11
  %T Triadic Exploration and Exploration with Multiple Experts
  %X Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.
  %@ 978-3-030-77867-5
  

• 1.

  D{ü}rrschnabel, D., Koyda, M., Stumme, G.: Attribute Selection Using Contranominal Scales. In: Braun, T., Gehrke, M., Hanika, T., and Hernandez, N. (eds.) Graph-Based Representation and Reasoning. pp. 127–141. Springer International Publishing, Cham (2021).

  BibTeXEndNoteBibSonomy

  Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.

  @inproceedings{10.1007/978-3-030-86982-3_10,
  abstract = {Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.},
  address = {Cham},
  author = {D{ü}rrschnabel, Dominik and Koyda, Maren and Stumme, Gerd},
  booktitle = {Graph-Based Representation and Reasoning},
  editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},
  keywords = 2021,
  pages = {127--141},
  publisher = {Springer International Publishing},
  title = {Attribute Selection Using Contranominal Scales},
  year = 2021
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-030-86982-3_10
  %A D{ü}rrschnabel, Dominik
  %A Koyda, Maren
  %A Stumme, Gerd
  %B Graph-Based Representation and Reasoning
  %C Cham
  %D 2021
  %E Braun, Tanya
  %E Gehrke, Marcel
  %E Hanika, Tom
  %E Hernandez, Nathalie
  %I Springer International Publishing
  %P 127--141
  %T Attribute Selection Using Contranominal Scales
  %X Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that {\$}{\$}{\backslash}delta {\$}{\$}$\delta$-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.
  %@ 978-3-030-86982-3
  

• 1.

  Dürrschnabel, D., Stumme, G.: Force-Directed Layout of Order Diagrams Using Dimensional Reduction. In: Braud, A., Buzmakov, A., Hanika, T., and Le Ber, F. (eds.) Formal Concept Analysis. pp. 224–240. Springer International Publishing, Cham (2021).

  BibTeXEndNoteBibSonomy

  Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.

  @inproceedings{10.1007/978-3-030-77867-5_14,
  abstract = {Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.},
  address = {Cham},
  author = {Dürrschnabel, Dominik and Stumme, Gerd},
  booktitle = {Formal Concept Analysis},
  editor = {Braud, Agn{è}s and Buzmakov, Aleksey and Hanika, Tom and Le Ber, Florence},
  keywords = {itegpub},
  pages = {224--240},
  publisher = {Springer International Publishing},
  title = {Force-Directed Layout of Order Diagrams Using Dimensional Reduction},
  year = 2021
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-030-77867-5_14
  %A Dürrschnabel, Dominik
  %A Stumme, Gerd
  %B Formal Concept Analysis
  %C Cham
  %D 2021
  %E Braud, Agn{è}s
  %E Buzmakov, Aleksey
  %E Hanika, Tom
  %E Le Ber, Florence
  %I Springer International Publishing
  %P 224--240
  %T Force-Directed Layout of Order Diagrams Using Dimensional Reduction
  %X Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.
  %@ 978-3-030-77867-5
  

• 1.

  Hanika, T., Hirth, J.: Quantifying the Conceptual Error in Dimensionality Reduction. In: Braun, T., Gehrke, M., Hanika, T., and Hernandez, N. (eds.) Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings. pp. 105–118. Springer (2021). https://doi.org/10.1007/978-3-030-86982-3_8.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/iccs/HanikaH21,
  author = {Hanika, Tom and Hirth, Johannes},
  booktitle = {Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings},
  editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},
  keywords = {selected},
  pages = {105--118},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Quantifying the Conceptual Error in Dimensionality Reduction},
  volume = 12879,
  year = 2021
  }
  

  %0 Conference Paper
  %1 DBLP:conf/iccs/HanikaH21
  %A Hanika, Tom
  %A Hirth, Johannes
  %B Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings
  %D 2021
  %E Braun, Tanya
  %E Gehrke, Marcel
  %E Hanika, Tom
  %E Hernandez, Nathalie
  %I Springer
  %P 105--118
  %R 10.1007/978-3-030-86982-3_8
  %T Quantifying the Conceptual Error in Dimensionality Reduction
  %U https://doi.org/10.1007/978-3-030-86982-3_8
  %V 12879
  

• 1.

  Braud, A., Buzmakov, A., Hanika, T., Ber, F.L. eds.: Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings. Springer (2021). https://doi.org/10.1007/978-3-030-77867-5.

  URLBibTeXEndNoteDOIBibSonomy

  @proceedings{DBLP:conf/icfca/2021,
  editor = {Braud, Agn{{è}}s and Buzmakov, Aleksey and Hanika, Tom and Ber, Florence Le},
  keywords = {itegpub},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings},
  volume = 12733,
  year = 2021
  }
  

  %0 Conference Proceedings
  %1 DBLP:conf/icfca/2021
  %B Lecture Notes in Computer Science
  %D 2021
  %E Braud, Agn{{è}}s
  %E Buzmakov, Aleksey
  %E Hanika, Tom
  %E Ber, Florence Le
  %I Springer
  %R 10.1007/978-3-030-77867-5
  %T Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings
  %U https://doi.org/10.1007/978-3-030-77867-5
  %V 12733
  %@ 978-3-030-77866-8
  

• 1.

  Stubbemann, M., Stumme, G.: LG4AV: Combining Language Models and Graph Neural Networks for Author Verification. (2021).

  URLBibTeXEndNoteBibSonomy

  @article{stubbemann2021lg4av,
  author = {Stubbemann, Maximilian and Stumme, Gerd},
  keywords = {itegpub},
  title = {LG4AV: Combining Language Models and Graph Neural Networks for Author Verification},
  year = 2021
  }
  

  %0 Journal Article
  %1 stubbemann2021lg4av
  %A Stubbemann, Maximilian
  %A Stumme, Gerd
  %D 2021
  %T LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
  %U https://arxiv.org/abs/2109.01479
  

• 1.

  Dürrschnabel, D., Hanika, T., Stubbemann, M.: FCA2VEC: Embedding Techniques for Formal Concept Analysis. Presented at the (2021).

  BibTeXEndNoteBibSonomy

  @inbook{durrschnabel2021fca2vec,
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stubbemann, Maximilian},
  keywords = {fca2vec},
  title = {FCA2VEC: Embedding Techniques for Formal Concept Analysis},
  year = 2021
  }
  

  %0 Book Section
  %1 durrschnabel2021fca2vec
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stubbemann, Maximilian
  %D 2021
  %T FCA2VEC: Embedding Techniques for Formal Concept Analysis
  

• 1.

  Schäfermeier, B., Stumme, G., Hanika, T.: Towards Explainable Scientific Venue Recommendations, http://arxiv.org/abs/2109.11343, (2021).

  URLBibTeXEndNoteBibSonomy

  Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.

  @misc{schafermeier2021towards,
  abstract = {Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.},
  author = {Schäfermeier, Bastian and Stumme, Gerd and Hanika, Tom},
  keywords = {venue_recommendations},
  note = {cite arxiv:2109.11343},
  title = {Towards Explainable Scientific Venue Recommendations},
  year = 2021
  }
  

  %0 Generic
  %1 schafermeier2021towards
  %A Schäfermeier, Bastian
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2021
  %T Towards Explainable Scientific Venue Recommendations
  %U http://arxiv.org/abs/2109.11343
  %X Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.
  

2020

• 1.

  Borchmann, D., Hanika, T., Obiedkov, S.: Probably approximately correct learning of Horn envelopes from queries. Discrete Applied Mathematics. 273, 30–42 (2020). https://doi.org/https://doi.org/10.1016/j.dam.2019.02.036.

  URLBibTeXEndNoteDOIBibSonomy

  We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.

  @article{BORCHMANN202030,
  abstract = {We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.},
  author = {Borchmann, Daniel and Hanika, Tom and Obiedkov, Sergei},
  journal = {Discrete Applied Mathematics},
  keywords = {itegpub},
  note = {Advances in Formal Concept Analysis: Traces of CLA 2016},
  pages = {30 - 42},
  title = {Probably approximately correct learning of Horn envelopes from queries},
  volume = 273,
  year = 2020
  }
  

  %0 Journal Article
  %1 BORCHMANN202030
  %A Borchmann, Daniel
  %A Hanika, Tom
  %A Obiedkov, Sergei
  %D 2020
  %J Discrete Applied Mathematics
  %P 30 - 42
  %R https://doi.org/10.1016/j.dam.2019.02.036
  %T Probably approximately correct learning of Horn envelopes from queries
  %U http://www.sciencedirect.com/science/article/pii/S0166218X19301295
  %V 273
  %X We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.
  

• 1.

  Schäfermeier, B., Stumme, G., Hanika, T.: Topic Space Trajectories: A case study on machine learning literature, http://arxiv.org/abs/2010.12294, (2020).

  URLBibTeXEndNoteBibSonomy

  The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present \emph{topic space trajectories}, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.

  @misc{schafermeier2020topic,
  abstract = {The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present \emph{topic space trajectories}, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.},
  author = {Schäfermeier, Bastian and Stumme, Gerd and Hanika, Tom},
  keywords = {itegpub},
  note = {cite arxiv:2010.12294Comment: 36 pages, 8 figures},
  title = {Topic Space Trajectories: A case study on machine learning literature},
  year = 2020
  }
  

  %0 Generic
  %1 schafermeier2020topic
  %A Schäfermeier, Bastian
  %A Stumme, Gerd
  %A Hanika, Tom
  %D 2020
  %T Topic Space Trajectories: A case study on machine learning literature
  %U http://arxiv.org/abs/2010.12294
  %X The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present \emph{topic space trajectories}, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.
  

• 1.

  Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic Dimension of Geometric Data Sets. Accepted for publication in: Tohoku Mathematical Journal. (2020).

  URLBibTeXEndNoteBibSonomy

  The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.

  @article{hanika2018intrinsic,
  abstract = {The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.},
  author = {Hanika, Tom and Schneider, Friedrich Martin and Stumme, Gerd},
  journal = {Accepted for publication in: Tohoku Mathematical Journal},
  keywords = {itegpub},
  note = {cite arxiv:1801.07985Comment: v2: completely rewritten 28 pages, 3 figures, 2 tables},
  title = {Intrinsic Dimension of Geometric Data Sets},
  year = 2020
  }
  

  %0 Journal Article
  %1 hanika2018intrinsic
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %A Stumme, Gerd
  %D 2020
  %J Accepted for publication in: Tohoku Mathematical Journal
  %T Intrinsic Dimension of Geometric Data Sets
  %U http://arxiv.org/abs/1801.07985
  %X The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
  

• 1.

  Stubbemann, M., Hanika, T., Stumme, G.: Orometric Methods in Bounded Metric Data. In: Berthold, M.R., Feelders, A., and Krempl, G. (eds.) Advances in Intelligent Data Analysis {XVIII} - 18th International Symposium on Intelligent Data Analysis, {IDA} 2020, Konstanz, Germany, April 27-29, 2020, Proceedings. pp. 496–508. Springer (2020). https://doi.org/10.1007/978-3-030-44584-3_39.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/ida/StubbemannHS20,
  author = {Stubbemann, Maximilian and Hanika, Tom and Stumme, Gerd},
  booktitle = {Advances in Intelligent Data Analysis {XVIII} - 18th International Symposium on Intelligent Data Analysis, {IDA} 2020, Konstanz, Germany, April 27-29, 2020, Proceedings},
  editor = {Berthold, Michael R. and Feelders, Ad and Krempl, Georg},
  keywords = {itegpub},
  pages = {496--508},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Orometric Methods in Bounded Metric Data},
  volume = 12080,
  year = 2020
  }
  

  %0 Conference Paper
  %1 DBLP:conf/ida/StubbemannHS20
  %A Stubbemann, Maximilian
  %A Hanika, Tom
  %A Stumme, Gerd
  %B Advances in Intelligent Data Analysis {XVIII} - 18th International Symposium on Intelligent Data Analysis, {IDA} 2020, Konstanz, Germany, April 27-29, 2020, Proceedings
  %D 2020
  %E Berthold, Michael R.
  %E Feelders, Ad
  %E Krempl, Georg
  %I Springer
  %P 496--508
  %R 10.1007/978-3-030-44584-3_39
  %T Orometric Methods in Bounded Metric Data
  %U https://doi.org/10.1007/978-3-030-44584-3\_39
  %V 12080
  

• 1.

  Stumme, G.: On Ordinal Data Science and its role in Socially Acceptable ICTDesign. In: Hentschel, A., Hornung, G., and Jandt, S. (eds.) Mensch – Technik – Umwelt: Verantwortung für eine sozialverträgliche Zukunft, Festschrift für Alexander Roßnagel zum 70. Geburtstag. pp. 181–198. Nomos, Baden-Baden (2020).

  BibTeXEndNoteBibSonomy

  Comparing and ordering things is a basal ability of mankind for organizing its physical and social environment. While many hierarchical relationships can be derived from numerical measures like length or voltage, many others cannot appropriately be captured this way. We argue that the newly emerging field of data science up to now lacks engagement in developing analysis methods for such ordinal data. By the example of an already existing approach in this domain, Formal Concept Analysis, we will discuss its capabilities as a knowledge representation and argue – based on its philosophical foundations – why it is an important building block for socially acceptable IT design.

  @inbook{stumme2020ordinal,
  abstract = {Comparing and ordering things is a basal ability of mankind for organizing its physical and social environment. While many hierarchical relationships can be derived from numerical measures like length or voltage, many others cannot appropriately be captured this way. We argue that the newly emerging field of data science up to now lacks engagement in developing analysis methods for such ordinal data. By the example of an already existing approach in this domain, Formal Concept Analysis, we will discuss its capabilities as a knowledge representation and argue – based on its philosophical foundations – why it is an important building block for socially acceptable IT design.},
  address = {Baden-Baden},
  author = {Stumme, G.},
  booktitle = {Mensch – Technik – Umwelt: Verantwortung für eine sozialverträgliche Zukunft, Festschrift für Alexander Roßnagel zum 70. Geburtstag},
  editor = {Hentschel, A. and Hornung, G. and Jandt, S.},
  keywords = {itegpub},
  pages = {181-198},
  publisher = {Nomos},
  title = {On Ordinal Data Science and its role in Socially Acceptable ICTDesign},
  year = 2020
  }
  

  %0 Book Section
  %1 stumme2020ordinal
  %A Stumme, G.
  %B Mensch – Technik – Umwelt: Verantwortung für eine sozialverträgliche Zukunft, Festschrift für Alexander Roßnagel zum 70. Geburtstag
  %C Baden-Baden
  %D 2020
  %E Hentschel, A.
  %E Hornung, G.
  %E Jandt, S.
  %I Nomos
  %P 181-198
  %T On Ordinal Data Science and its role in Socially Acceptable ICTDesign
  %X Comparing and ordering things is a basal ability of mankind for organizing its physical and social environment. While many hierarchical relationships can be derived from numerical measures like length or voltage, many others cannot appropriately be captured this way. We argue that the newly emerging field of data science up to now lacks engagement in developing analysis methods for such ordinal data. By the example of an already existing approach in this domain, Formal Concept Analysis, we will discuss its capabilities as a knowledge representation and argue – based on its philosophical foundations – why it is an important building block for socially acceptable IT design.
  %@ 978-3-8487-7014-4
  

• 1.

  Felde, M., Hanika, T., Stumme, G.: Null Models for Formal Contexts. Information. 11, 135 (2020).

  URLBibTeXEndNoteBibSonomy

  @article{felde2020null,
  author = {Felde, Maximilian and Hanika, Tom and Stumme, Gerd},
  journal = {Information},
  keywords = {itegpub},
  number = 3,
  pages = 135,
  publisher = {Multidisciplinary Digital Publishing Institute},
  title = {Null Models for Formal Contexts},
  volume = 11,
  year = 2020
  }
  

  %0 Journal Article
  %1 felde2020null
  %A Felde, Maximilian
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2020
  %I Multidisciplinary Digital Publishing Institute
  %J Information
  %N 3
  %P 135
  %T Null Models for Formal Contexts
  %U https://www.mdpi.com/2078-2489/11/3/135
  %V 11
  

2019

• 1.

  Dürrschnabel, D., Hanika, T., Stumme, G.: Drawing Order Diagrams Through Two-Dimension Extension, http://arxiv.org/abs/1906.06208, (2019).

  URLBibTeXEndNoteBibSonomy

  Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work we present the novel algorithm DimDraw to draw order diagrams. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of a corresponding graph.

  @misc{durrschnabel2019drawing,
  abstract = {Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work we present the novel algorithm DimDraw to draw order diagrams. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of a corresponding graph.},
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stumme, Gerd},
  keywords = {itegpub},
  note = {cite arxiv:1906.06208Comment: 16 pages, 12 Figures},
  title = {Drawing Order Diagrams Through Two-Dimension Extension},
  year = 2019
  }
  

  %0 Generic
  %1 durrschnabel2019drawing
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2019
  %T Drawing Order Diagrams Through Two-Dimension Extension
  %U http://arxiv.org/abs/1906.06208
  %X Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work we present the novel algorithm DimDraw to draw order diagrams. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of a corresponding graph.
  

• 1.

  Hanika, T., Hirth, J.: Conexp-Clj - A Research Tool for FCA. In: Cristea, D., Ber, F.L., Missaoui, R., Kwuida, L., and Sertkaya, B. (eds.) ICFCA (Supplements). pp. 70–75. CEUR-WS.org (2019).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{conf/icfca/HanikaH19,
  author = {Hanika, Tom and Hirth, Johannes},
  booktitle = {ICFCA (Supplements)},
  crossref = {conf/icfca/2019suppl},
  editor = {Cristea, Diana and Ber, Florence Le and Missaoui, Rokia and Kwuida, Léonard and Sertkaya, Baris},
  keywords = {itegpub},
  pages = {70-75},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {Conexp-Clj - A Research Tool for FCA.},
  volume = 2378,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/icfca/HanikaH19
  %A Hanika, Tom
  %A Hirth, Johannes
  %B ICFCA (Supplements)
  %D 2019
  %E Cristea, Diana
  %E Ber, Florence Le
  %E Missaoui, Rokia
  %E Kwuida, Léonard
  %E Sertkaya, Baris
  %I CEUR-WS.org
  %P 70-75
  %T Conexp-Clj - A Research Tool for FCA.
  %U http://dblp.uni-trier.de/db/conf/icfca/icfca2019suppl.html#HanikaH19
  %V 2378
  

• 1.

  Felde, M., Hanika, T.: Formal Context Generation Using Dirichlet Distributions. In: Endres, D., Alam, M., and Sotropa, D. (eds.) ICCS. pp. 57–71. Springer (2019). https://doi.org/10.1007/978-3-030-23182-8_5.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/iccs/FeldeH19,
  author = {Felde, Maximilian and Hanika, Tom},
  booktitle = {ICCS},
  crossref = {conf/iccs/2019},
  editor = {Endres, Dominik and Alam, Mehwish and Sotropa, Diana},
  keywords = {itegpub},
  pages = {57-71},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Formal Context Generation Using Dirichlet Distributions.},
  volume = 11530,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/iccs/FeldeH19
  %A Felde, Maximilian
  %A Hanika, Tom
  %B ICCS
  %D 2019
  %E Endres, Dominik
  %E Alam, Mehwish
  %E Sotropa, Diana
  %I Springer
  %P 57-71
  %R 10.1007/978-3-030-23182-8_5
  %T Formal Context Generation Using Dirichlet Distributions.
  %U http://dblp.uni-trier.de/db/conf/iccs/iccs2019.html#FeldeH19
  %V 11530
  %@ 978-3-030-23182-8
  

• 1.

  Hanika, T.: Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis., (2019). https://doi.org/10.17170/kobra-20190213189.

  BibTeXEndNoteDOIBibSonomy

  @phdthesis{phd/dnb/Hanika19,
  author = {Hanika, Tom},
  keywords = {itegpub},
  school = {University of Kassel, Germany},
  title = {Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis.},
  year = 2019
  }
  

  %0 Thesis
  %1 phd/dnb/Hanika19
  %A Hanika, Tom
  %D 2019
  %R 10.17170/kobra-20190213189
  %T Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis.
  

• 1.

  Dürrschnabel, D., Hanika, T., Stubbemann, M.: FCA2VEC: Embedding Techniques for Formal Concept Analysis, http://arxiv.org/abs/1911.11496, (2019).

  URLBibTeXEndNoteBibSonomy

  Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding latent semantic analysis recent approaches like word2vec or node2vec are well established tools in this realm. In the present paper we add to this line of research by introducing fca2vec, a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computational feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA community.

  @misc{durrschnabel2019fca2vec,
  abstract = {Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding latent semantic analysis recent approaches like word2vec or node2vec are well established tools in this realm. In the present paper we add to this line of research by introducing fca2vec, a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computational feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA community.},
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stubbemann, Maximilian},
  keywords = {itegpub},
  note = {cite arxiv:1911.11496Comment: 25 pages},
  title = {FCA2VEC: Embedding Techniques for Formal Concept Analysis},
  year = 2019
  }
  

  %0 Generic
  %1 durrschnabel2019fca2vec
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stubbemann, Maximilian
  %D 2019
  %T FCA2VEC: Embedding Techniques for Formal Concept Analysis
  %U http://arxiv.org/abs/1911.11496
  %X Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding latent semantic analysis recent approaches like word2vec or node2vec are well established tools in this realm. In the present paper we add to this line of research by introducing fca2vec, a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computational feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA community.
  

• 1.

  Felde, M., Stumme, G.: Interactive Collaborative Exploration using Incomplete Contexts, https://arxiv.org/abs/1908.08740, (2019).

  URLBibTeXEndNoteBibSonomy

  A common representation of information about relations of objects and attributes in knowledge domains are data-tables. The structure of such information can be analysed using Formal Concept Analysis (FCA). Attribute exploration is a knowledge acquisition method from FCA that reveals dependencies in a set of attributes with help of a domain expert. However, in general no single expert is capable (time- and knowledge-wise) of exploring knowledge domains alone. Therefore it is important to develop methods that allow multiple experts to explore domains together. To this end we build upon results on representation of incomplete knowledge [2, 8-10], adapt the corresponding version of attribute exploration to fit the setting of multiple experts and suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. Furthermore we discuss ways of comparing collaboration strategies and suggest avenues for future research.

  @misc{felde2019interactive,
  abstract = {A common representation of information about relations of objects and attributes in knowledge domains are data-tables. The structure of such information can be analysed using Formal Concept Analysis (FCA). Attribute exploration is a knowledge acquisition method from FCA that reveals dependencies in a set of attributes with help of a domain expert. However, in general no single expert is capable (time- and knowledge-wise) of exploring knowledge domains alone. Therefore it is important to develop methods that allow multiple experts to explore domains together. To this end we build upon results on representation of incomplete knowledge [2, 8-10], adapt the corresponding version of attribute exploration to fit the setting of multiple experts and suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. Furthermore we discuss ways of comparing collaboration strategies and suggest avenues for future research.},
  author = {Felde, Maximilian and Stumme, Gerd},
  keywords = {itegpub},
  title = {Interactive Collaborative Exploration using Incomplete Contexts},
  year = 2019
  }
  

  %0 Generic
  %1 felde2019interactive
  %A Felde, Maximilian
  %A Stumme, Gerd
  %D 2019
  %T Interactive Collaborative Exploration using Incomplete Contexts
  %U https://arxiv.org/abs/1908.08740
  %X A common representation of information about relations of objects and attributes in knowledge domains are data-tables. The structure of such information can be analysed using Formal Concept Analysis (FCA). Attribute exploration is a knowledge acquisition method from FCA that reveals dependencies in a set of attributes with help of a domain expert. However, in general no single expert is capable (time- and knowledge-wise) of exploring knowledge domains alone. Therefore it is important to develop methods that allow multiple experts to explore domains together. To this end we build upon results on representation of incomplete knowledge [2, 8-10], adapt the corresponding version of attribute exploration to fit the setting of multiple experts and suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. Furthermore we discuss ways of comparing collaboration strategies and suggest avenues for future research.
  

• 1.

  Hanika, T., Kibanov, M., Kropf, J., Laser, S.: Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt popul{ä}r und besonders interessant f{ü}r die Forschung geworden ist. In: Kropf, J. and Laser, S. (eds.) Digitale Bewertungspraktiken. pp. 165–188. Springer (2019).

  BibTeXEndNoteBibSonomy

  @incollection{hanika2019denke,
  author = {Hanika, Tom and Kibanov, Mark and Kropf, Jonathan and Laser, Stefan},
  booktitle = {Digitale Bewertungspraktiken},
  editor = {Kropf, Jonathan and Laser, Stefan},
  keywords = {bewertungspraktiken},
  pages = {165--188},
  publisher = {Springer},
  title = {Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt popul{ä}r und besonders interessant f{ü}r die Forschung geworden ist.},
  year = 2019
  }
  

  %0 Book Section
  %1 hanika2019denke
  %A Hanika, Tom
  %A Kibanov, Mark
  %A Kropf, Jonathan
  %A Laser, Stefan
  %B Digitale Bewertungspraktiken
  %D 2019
  %E Kropf, Jonathan
  %E Laser, Stefan
  %I Springer
  %P 165--188
  %T Ich denke, es ist wichtig zu verstehen, warum die Netzwerkanalyse jetzt popul{ä}r und besonders interessant f{ü}r die Forschung geworden ist.
  

• 1.

  Hanika, T., Herde, M., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning - A clarification of terms and a differentiation from other research fields. CoRR. abs/1905.07264, (2019).

  URLBibTeXEndNoteBibSonomy

  @article{journals/corr/abs-1905-07264,
  author = {Hanika, Tom and Herde, Marek and Kuhn, Jochen and Leimeister, Jan Marco and Lukowicz, Paul and Oeste-Reiß, Sarah and Schmidt, Albrecht and Sick, Bernhard and Stumme, Gerd and Tomforde, Sven and Zweig, Katharina Anna},
  journal = {CoRR},
  keywords = {itegpub},
  title = {Collaborative Interactive Learning - A clarification of terms and a differentiation from other research fields.},
  volume = {abs/1905.07264},
  year = 2019
  }
  

  %0 Journal Article
  %1 journals/corr/abs-1905-07264
  %A Hanika, Tom
  %A Herde, Marek
  %A Kuhn, Jochen
  %A Leimeister, Jan Marco
  %A Lukowicz, Paul
  %A Oeste-Reiß, Sarah
  %A Schmidt, Albrecht
  %A Sick, Bernhard
  %A Stumme, Gerd
  %A Tomforde, Sven
  %A Zweig, Katharina Anna
  %D 2019
  %J CoRR
  %T Collaborative Interactive Learning - A clarification of terms and a differentiation from other research fields.
  %U http://dblp.uni-trier.de/db/journals/corr/corr1905.html#abs-1905-07264
  %V abs/1905.07264
  

• 1.

  Hanika, T., Marx, M., Stumme, G.: Discovering Implicational Knowledge in Wikidata. In: Cristea, D., Ber, F.L., and Sertkaya, B. (eds.) Formal Concept Analysis - 15th International Conference, {ICFCA} 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings. pp. 315–323. Springer (2019). https://doi.org/10.1007/978-3-030-21462-3_21.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{DBLP:conf/icfca/Hanika0S19,
  author = {Hanika, Tom and Marx, Maximilian and Stumme, Gerd},
  booktitle = {Formal Concept Analysis - 15th International Conference, {ICFCA} 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings},
  editor = {Cristea, Diana and Ber, Florence Le and Sertkaya, Baris},
  keywords = {kdepub},
  pages = {315--323},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Discovering Implicational Knowledge in Wikidata},
  volume = 11511,
  year = 2019
  }
  

  %0 Conference Paper
  %1 DBLP:conf/icfca/Hanika0S19
  %A Hanika, Tom
  %A Marx, Maximilian
  %A Stumme, Gerd
  %B Formal Concept Analysis - 15th International Conference, {ICFCA} 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings
  %D 2019
  %E Cristea, Diana
  %E Ber, Florence Le
  %E Sertkaya, Baris
  %I Springer
  %P 315--323
  %R 10.1007/978-3-030-21462-3_21
  %T Discovering Implicational Knowledge in Wikidata
  %U https://doi.org/10.1007/978-3-030-21462-3_21
  %V 11511
  

• 1.

  Schäfermeier, B., Hanika, T., Stumme, G.: Distances for WiFi Based Topological Indoor Mapping. In: 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), November 12--14, 2019, Houston, TX, USA (2019). https://doi.org/10.1145/3360774.3360780.

  BibTeXEndNoteDOIBibSonomy

  For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.

  @inproceedings{schafermeier2019distances,
  abstract = {For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.},
  author = {Schäfermeier, Bastian and Hanika, Tom and Stumme, Gerd},
  booktitle = {16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), November 12--14, 2019, Houston, TX, USA},
  keywords = {itegpub},
  title = {Distances for WiFi Based Topological Indoor Mapping},
  year = 2019
  }
  

  %0 Conference Paper
  %1 schafermeier2019distances
  %A Schäfermeier, Bastian
  %A Hanika, Tom
  %A Stumme, Gerd
  %B 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), November 12--14, 2019, Houston, TX, USA
  %D 2019
  %R 10.1145/3360774.3360780
  %T Distances for WiFi Based Topological Indoor Mapping
  %X For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.
  %@ 978-1-4503-7283-1/19/11
  

• 1.

  Stubbemann, M., Hanika, T., Stumme, G.: Orometric Methods in Bounded Metric Data. CoRR. abs/1907.09239, (2019).

  URLBibTeXEndNoteBibSonomy

  @article{journals/corr/abs-1907-09239,
  author = {Stubbemann, Maximilian and Hanika, Tom and Stumme, Gerd},
  journal = {CoRR},
  keywords = {preprint},
  note = {Accept for IDA 2020},
  title = {Orometric Methods in Bounded Metric Data.},
  volume = {abs/1907.09239},
  year = 2019
  }
  

  %0 Journal Article
  %1 journals/corr/abs-1907-09239
  %A Stubbemann, Maximilian
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2019
  %J CoRR
  %T Orometric Methods in Bounded Metric Data.
  %U http://dblp.uni-trier.de/db/journals/corr/corr1907.html#abs-1907-09239
  %V abs/1907.09239
  

• 1.

  Dürrschnabel, D., Hanika, T., Stumme, G.: DimDraw - A Novel Tool for Drawing Concept Lattices. In: Cristea, D., Ber, F.L., Missaoui, R., Kwuida, L., and Sertkaya, B. (eds.) ICFCA (Supplements). pp. 60–64. CEUR-WS.org (2019).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{conf/icfca/DurrschnabelHS19,
  author = {Dürrschnabel, Dominik and Hanika, Tom and Stumme, Gerd},
  booktitle = {ICFCA (Supplements)},
  crossref = {conf/icfca/2019suppl},
  editor = {Cristea, Diana and Ber, Florence Le and Missaoui, Rokia and Kwuida, Léonard and Sertkaya, Baris},
  keywords = {itegpub},
  pages = {60-64},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {DimDraw - A Novel Tool for Drawing Concept Lattices.},
  volume = 2378,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/icfca/DurrschnabelHS19
  %A Dürrschnabel, Dominik
  %A Hanika, Tom
  %A Stumme, Gerd
  %B ICFCA (Supplements)
  %D 2019
  %E Cristea, Diana
  %E Ber, Florence Le
  %E Missaoui, Rokia
  %E Kwuida, Léonard
  %E Sertkaya, Baris
  %I CEUR-WS.org
  %P 60-64
  %T DimDraw - A Novel Tool for Drawing Concept Lattices.
  %U http://dblp.uni-trier.de/db/conf/icfca/icfca2019suppl.html#DurrschnabelHS19
  %V 2378
  

• 1.

  Kibanov, M., Heiberger, R.H., R{ö}dder, S., Atzmueller, M., Stumme, G.: Social studies of scholarly life with sensor-based ethnographic observations. Scientometrics. (2019). https://doi.org/10.1007/s11192-019-03097-w.

  URLBibTeXEndNoteDOIBibSonomy

  Social network analysis is playing an increasingly important role in sociological studies. At the same time, new technologies such as wearable sensors make it possible to collect new types of social network data. We employed RFID tags to capture face-to-face interactions of participants of two consecutive Ph.D. retreats of a graduate school on climate research. We use this data in order to explore how it may support ethnographic observations and to gain further insights on scholarly interactions. The unique feature of the data is the opportunity to distinguish short and long conversations, which often have a different nature from a sociological point of view. Furthermore, an advantage of this data is the availability of socio-demographic, research-related, and situational attributes of participants. We show that, even though an interaction partner is often found rather randomly during coffee breaks of retreats, a strong homophily between participants from the same institutions or research areas exists. We identify cores of the networks and participants who play ambassador roles between communities, e.g., persons who visit the retreat for the second time are more likely to be ambassadors. Overall, we show the usefulness and potential of RFID tags for scientometric studies.

  @article{kibanov2019social,
  abstract = {Social network analysis is playing an increasingly important role in sociological studies. At the same time, new technologies such as wearable sensors make it possible to collect new types of social network data. We employed RFID tags to capture face-to-face interactions of participants of two consecutive Ph.D. retreats of a graduate school on climate research. We use this data in order to explore how it may support ethnographic observations and to gain further insights on scholarly interactions. The unique feature of the data is the opportunity to distinguish short and long conversations, which often have a different nature from a sociological point of view. Furthermore, an advantage of this data is the availability of socio-demographic, research-related, and situational attributes of participants. We show that, even though an interaction partner is often found rather randomly during coffee breaks of retreats, a strong homophily between participants from the same institutions or research areas exists. We identify cores of the networks and participants who play ambassador roles between communities, e.g., persons who visit the retreat for the second time are more likely to be ambassadors. Overall, we show the usefulness and potential of RFID tags for scientometric studies.},
  author = {Kibanov, Mark and Heiberger, Raphael H. and R{ö}dder, Simone and Atzmueller, Martin and Stumme, Gerd},
  journal = {Scientometrics},
  keywords = {homophily},
  month = {05},
  title = {Social studies of scholarly life with sensor-based ethnographic observations},
  year = 2019
  }
  

  %0 Journal Article
  %1 kibanov2019social
  %A Kibanov, Mark
  %A Heiberger, Raphael H.
  %A R{ö}dder, Simone
  %A Atzmueller, Martin
  %A Stumme, Gerd
  %D 2019
  %J Scientometrics
  %R 10.1007/s11192-019-03097-w
  %T Social studies of scholarly life with sensor-based ethnographic observations
  %U https://doi.org/10.1007/s11192-019-03097-w
  %X Social network analysis is playing an increasingly important role in sociological studies. At the same time, new technologies such as wearable sensors make it possible to collect new types of social network data. We employed RFID tags to capture face-to-face interactions of participants of two consecutive Ph.D. retreats of a graduate school on climate research. We use this data in order to explore how it may support ethnographic observations and to gain further insights on scholarly interactions. The unique feature of the data is the opportunity to distinguish short and long conversations, which often have a different nature from a sociological point of view. Furthermore, an advantage of this data is the availability of socio-demographic, research-related, and situational attributes of participants. We show that, even though an interaction partner is often found rather randomly during coffee breaks of retreats, a strong homophily between participants from the same institutions or research areas exists. We identify cores of the networks and participants who play ambassador roles between communities, e.g., persons who visit the retreat for the second time are more likely to be ambassadors. Overall, we show the usefulness and potential of RFID tags for scientometric studies.
  

• 1.

  Schaefermeier, B., Hanika, T., Stumme, G.: Distances for wifi based topological indoor mapping. In: Proceedings of the 16th {EAI} International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. {ACM} (2019). https://doi.org/10.1145/3360774.3360780.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{Schaefermeier_2019,
  author = {Schaefermeier, Bastian and Hanika, Tom and Stumme, Gerd},
  booktitle = {Proceedings of the 16th {EAI} International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
  keywords = {itegpub},
  month = 11,
  publisher = {{ACM}},
  title = {Distances for wifi based topological indoor mapping},
  year = 2019
  }
  

  %0 Conference Paper
  %1 Schaefermeier_2019
  %A Schaefermeier, Bastian
  %A Hanika, Tom
  %A Stumme, Gerd
  %B Proceedings of the 16th {EAI} International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
  %D 2019
  %I {ACM}
  %R 10.1145/3360774.3360780
  %T Distances for wifi based topological indoor mapping
  %U https://doi.org/10.1145%2F3360774.3360780
  

• 1.

  Hanika, T., Koyda, M., Stumme, G.: Relevant Attributes in Formal Contexts. In: Endres, D., Alam, M., and Sotropa, D. (eds.) ICCS. pp. 102–116. Springer (2019). https://doi.org/10.1007/978-3-030-23182-8_8.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/iccs/HanikaKS19,
  author = {Hanika, Tom and Koyda, Maren and Stumme, Gerd},
  booktitle = {ICCS},
  crossref = {conf/iccs/2019},
  editor = {Endres, Dominik and Alam, Mehwish and Sotropa, Diana},
  keywords = {itegpub},
  pages = {102-116},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Relevant Attributes in Formal Contexts.},
  volume = 11530,
  year = 2019
  }
  

  %0 Conference Paper
  %1 conf/iccs/HanikaKS19
  %A Hanika, Tom
  %A Koyda, Maren
  %A Stumme, Gerd
  %B ICCS
  %D 2019
  %E Endres, Dominik
  %E Alam, Mehwish
  %E Sotropa, Diana
  %I Springer
  %P 102-116
  %R 10.1007/978-3-030-23182-8_8
  %T Relevant Attributes in Formal Contexts.
  %U http://dblp.uni-trier.de/db/conf/iccs/iccs2019.html#HanikaKS19
  %V 11530
  %@ 978-3-030-23182-8
  

2018

• 1.

  Kibanov, M., Becker, M., Müller, J., Atzmueller, M., Hotho, A., Stumme, G.: {Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data}. In: Proc. 33rd ACM Symposium On Applied Computing. ACM Press, New York, NY, USA (2018).

  BibTeXEndNoteBibSonomy

  @inproceedings{KBMAHS:18,
  address = {New York, NY, USA},
  author = {Kibanov, Mark and Becker, Martin and Müller, Jürgen and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proc. 33rd ACM Symposium On Applied Computing},
  keywords = {classification},
  publisher = {ACM Press},
  title = {{Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data}},
  year = 2018
  }
  

  %0 Conference Paper
  %1 KBMAHS:18
  %A Kibanov, Mark
  %A Becker, Martin
  %A Müller, Jürgen
  %A Atzmueller, Martin
  %A Hotho, Andreas
  %A Stumme, Gerd
  %B Proc. 33rd ACM Symposium On Applied Computing
  %C New York, NY, USA
  %D 2018
  %I ACM Press
  %T {Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data}
  

• 1.

  Thiele, L., Atzmueller, M., Stumme, G., Kauffeld, S.: {Frequent and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Developmental Peer Network Relationships}. Psychology. (2018).

  BibTeXEndNoteBibSonomy

  @article{TASK:18,
  author = {Thiele, Lisa and Atzmueller, Martin and Stumme, Gerd and Kauffeld, Simone},
  journal = {Psychology},
  keywords = {face-to-face},
  month = {(In Press)},
  title = {{Frequent and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Developmental Peer Network Relationships}},
  year = 2018
  }
  

  %0 Journal Article
  %1 TASK:18
  %A Thiele, Lisa
  %A Atzmueller, Martin
  %A Stumme, Gerd
  %A Kauffeld, Simone
  %D 2018
  %J Psychology
  %T {Frequent and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Developmental Peer Network Relationships}
  

• 1.

  Schmidt, A., Stumme, G.: Prominence and Dominance in Networks. In: Faron Zucker, C., Ghidini, C., Napoli, A., and Yannick, T. (eds.) Proceedings of the 21th International Conference on Knowledge Engineering and Knowledge Management (EKAW). pp. 370–385. Springer (2018).

  BibTeXEndNoteBibSonomy

  @inproceedings{schmidt2018prominence,
  author = {Schmidt, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 21th International Conference on Knowledge Engineering and Knowledge Management (EKAW)},
  editor = {Faron Zucker, Catherine and Ghidini, Chiara and Napoli, Amedeo and Yannick, Toussaint},
  keywords = {topographic},
  pages = {370-385},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Prominence and Dominance in Networks},
  year = 2018
  }
  

  %0 Conference Paper
  %1 schmidt2018prominence
  %A Schmidt, Andreas
  %A Stumme, Gerd
  %B Proceedings of the 21th International Conference on Knowledge Engineering and Knowledge Management (EKAW)
  %D 2018
  %E Faron Zucker, Catherine
  %E Ghidini, Chiara
  %E Napoli, Amedeo
  %E Yannick, Toussaint
  %I Springer
  %P 370-385
  %T Prominence and Dominance in Networks
  

• 1.

  Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic Dimension of Geometric Data Sets, http://arxiv.org/abs/1801.07985, (2018).

  URLBibTeXEndNoteBibSonomy

  The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.

  @misc{hanika2018intrinsic,
  abstract = {The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.},
  author = {Hanika, Tom and Schneider, Friedrich Martin and Stumme, Gerd},
  keywords = {itegpub},
  note = {cite arxiv:1801.07985Comment: v2: completely rewritten 28 pages, 3 figures, 2 tables},
  title = {Intrinsic Dimension of Geometric Data Sets},
  year = 2018
  }
  

  %0 Generic
  %1 hanika2018intrinsic
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %A Stumme, Gerd
  %D 2018
  %T Intrinsic Dimension of Geometric Data Sets
  %U http://arxiv.org/abs/1801.07985
  %X The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
  

• 1.

  Schäfermeier, B., Hanika, T., Stumme, G.: Distances for WiFi Based Topological Indoor Mapping, http://arxiv.org/abs/1809.07405, (2018).

  URLBibTeXEndNoteBibSonomy

  For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.

  @misc{schafermeier2018distances,
  abstract = {For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.},
  author = {Schäfermeier, Bastian and Hanika, Tom and Stumme, Gerd},
  keywords = {itegpub},
  note = {cite arxiv:1809.07405Comment: 10 pages, 6 figures},
  title = {Distances for WiFi Based Topological Indoor Mapping},
  year = 2018
  }
  

  %0 Generic
  %1 schafermeier2018distances
  %A Schäfermeier, Bastian
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2018
  %T Distances for WiFi Based Topological Indoor Mapping
  %U http://arxiv.org/abs/1809.07405
  %X For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.
  

• 1.

  Hanika, T., Koyda, M., Stumme, G.: Relevant Attributes in Formal Contexts. CoRR. abs/1812.08868, (2018).

  URLBibTeXEndNoteBibSonomy

  @article{journals/corr/abs-1812-08868,
  author = {Hanika, Tom and Koyda, Maren and Stumme, Gerd},
  journal = {CoRR},
  keywords = {relevance},
  note = {Accepted for ICCS'19},
  title = {Relevant Attributes in Formal Contexts.},
  volume = {abs/1812.08868},
  year = 2018
  }
  

  %0 Journal Article
  %1 journals/corr/abs-1812-08868
  %A Hanika, Tom
  %A Koyda, Maren
  %A Stumme, Gerd
  %D 2018
  %J CoRR
  %T Relevant Attributes in Formal Contexts.
  %U http://dblp.uni-trier.de/db/journals/corr/corr1812.html#abs-1812-08868
  %V abs/1812.08868
  

• 1.

  Doerfel, S., Hanika, T., Stumme, G.: Clones in Social Networks. CoRR. abs/1802.07849, (2018).

  URLBibTeXEndNoteBibSonomy

  It is well known that any bipartite (social) network can be regarded as a formal context $(G,M,I)$. Therefore, such networks give raise to formal concept lattices which can be investigated utilizing the toolset of Formal Concept Analysis (FCA). In particular, the notion of clones in closure systems on $M$, i.e., pairwise interchangeable attributes that leave the closure system unchanged, suggests itself naturally as a candidate to be analyzed in the realm of FCA based social network analysis. In this study, we investigate the notion of clones in social networks. After building up some theoretical background for the clone relation in formal contexts we try to find clones in real word data sets. To this end, we provide an experimental evaluation on nine mostly well known social networks and provide some first insights on the impact of clones. We conclude our work by nourishing the understanding of clones by generalizing those to permutations of higher order.

  @article{doerfel2018clones,
  abstract = {It is well known that any bipartite (social) network can be regarded as a formal context $(G,M,I)$. Therefore, such networks give raise to formal concept lattices which can be investigated utilizing the toolset of Formal Concept Analysis (FCA). In particular, the notion of clones in closure systems on $M$, i.e., pairwise interchangeable attributes that leave the closure system unchanged, suggests itself naturally as a candidate to be analyzed in the realm of FCA based social network analysis. In this study, we investigate the notion of clones in social networks. After building up some theoretical background for the clone relation in formal contexts we try to find clones in real word data sets. To this end, we provide an experimental evaluation on nine mostly well known social networks and provide some first insights on the impact of clones. We conclude our work by nourishing the understanding of clones by generalizing those to permutations of higher order.},
  author = {Doerfel, Stephan and Hanika, Tom and Stumme, Gerd},
  journal = {CoRR},
  keywords = {networks},
  note = {cite arxiv:1802.07849Comment: 12 pages, 2 figures, 2 tables},
  title = {Clones in Social Networks},
  volume = {abs/1802.07849},
  year = 2018
  }
  

  %0 Journal Article
  %1 doerfel2018clones
  %A Doerfel, Stephan
  %A Hanika, Tom
  %A Stumme, Gerd
  %D 2018
  %J CoRR
  %T Clones in Social Networks
  %U http://arxiv.org/abs/1802.07849
  %V abs/1802.07849
  %X It is well known that any bipartite (social) network can be regarded as a formal context $(G,M,I)$. Therefore, such networks give raise to formal concept lattices which can be investigated utilizing the toolset of Formal Concept Analysis (FCA). In particular, the notion of clones in closure systems on $M$, i.e., pairwise interchangeable attributes that leave the closure system unchanged, suggests itself naturally as a candidate to be analyzed in the realm of FCA based social network analysis. In this study, we investigate the notion of clones in social networks. After building up some theoretical background for the clone relation in formal contexts we try to find clones in real word data sets. To this end, we provide an experimental evaluation on nine mostly well known social networks and provide some first insights on the impact of clones. We conclude our work by nourishing the understanding of clones by generalizing those to permutations of higher order.
  

• 1.

  Axenovich, M., Dürrschnabel, D.: Subsets of vertices of the same size and the same maximum distance. 5, (2018).

  URLBibTeXEndNoteBibSonomy

  @article{axenovich2018subsets,
  author = {Axenovich, Maria and Dürrschnabel, Dominik},
  keywords = 2018,
  number = 2,
  title = {Subsets of vertices of the same size and the same maximum distance},
  volume = 5,
  year = 2018
  }
  

  %0 Journal Article
  %1 axenovich2018subsets
  %A Axenovich, Maria
  %A Dürrschnabel, Dominik
  %D 2018
  %N 2
  %T Subsets of vertices of the same size and the same maximum distance
  %U https://digitalcommons.georgiasouthern.edu/tag/vol5/iss2/7/
  %V 5
  

• 1.

  Borchmann, D., Hanika, T., Obiedkov, S.: Probably approximately correct learning of Horn envelopes from queries. CoRR. abs/1807.06149, (2018).

  URLBibTeXEndNoteBibSonomy

  We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.

  @article{journals/corr/abs-1807-06149,
  abstract = {We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.},
  author = {Borchmann, Daniel and Hanika, Tom and Obiedkov, Sergei},
  journal = {CoRR},
  keywords = {preprint},
  note = {cite arxiv:1807.06149Comment: 21 pages, 1 figure},
  title = {Probably approximately correct learning of Horn envelopes from queries.},
  volume = {abs/1807.06149},
  year = 2018
  }
  

  %0 Journal Article
  %1 journals/corr/abs-1807-06149
  %A Borchmann, Daniel
  %A Hanika, Tom
  %A Obiedkov, Sergei
  %D 2018
  %J CoRR
  %T Probably approximately correct learning of Horn envelopes from queries.
  %U http://dblp.uni-trier.de/db/journals/corr/corr1807.html#abs-1807-06149
  %V abs/1807.06149
  %X We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.
  

• 1.

  Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic dimension and its application to association rules. CoRR. abs/1805.05714, (2018).

  URLBibTeXEndNoteBibSonomy

  The curse of dimensionality in the realm of association rules is twofold. Firstly, we have the well known exponential increase in computational complexity with increasing item set size. Secondly, there is a \emph{related curse} concerned with the distribution of (spare) data itself in high dimension. The former problem is often coped with by projection, i.e., feature selection, whereas the best known strategy for the latter is avoidance. This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures. This recent development enables the application of various other methods from geometric analysis to be investigated and applied in machine learning procedures in the presence of high dimension.

  @article{hanika2018intrinsic,
  abstract = {The curse of dimensionality in the realm of association rules is twofold. Firstly, we have the well known exponential increase in computational complexity with increasing item set size. Secondly, there is a \emph{related curse} concerned with the distribution of (spare) data itself in high dimension. The former problem is often coped with by projection, i.e., feature selection, whereas the best known strategy for the latter is avoidance. This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures. This recent development enables the application of various other methods from geometric analysis to be investigated and applied in machine learning procedures in the presence of high dimension.},
  author = {Hanika, Tom and Schneider, Friedrich Martin and Stumme, Gerd},
  journal = {CoRR},
  keywords = {dimension},
  note = {cite arxiv:1805.05714Comment: 4 pages, 1 figure},
  title = {Intrinsic dimension and its application to association rules},
  volume = {abs/1805.05714},
  year = 2018
  }
  

  %0 Journal Article
  %1 hanika2018intrinsic
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %A Stumme, Gerd
  %D 2018
  %J CoRR
  %T Intrinsic dimension and its application to association rules
  %U http://arxiv.org/abs/1805.05714
  %V abs/1805.05714
  %X The curse of dimensionality in the realm of association rules is twofold. Firstly, we have the well known exponential increase in computational complexity with increasing item set size. Secondly, there is a \emph{related curse} concerned with the distribution of (spare) data itself in high dimension. The former problem is often coped with by projection, i.e., feature selection, whereas the best known strategy for the latter is avoidance. This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures. This recent development enables the application of various other methods from geometric analysis to be investigated and applied in machine learning procedures in the presence of high dimension.
  

• 1.

  Navarro Bullock, B., Hotho, A., Stumme, G.: Accessing Information with Tags: Search and Ranking. In: Brusilovsky, P. and He, D. (eds.) Social Information Access: Systems and Technologies. pp. 310–343. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-90092-6_9.

  URLBibTeXEndNoteDOIBibSonomy

  With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.

  @inbook{NavarroBullock2018,
  abstract = {With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.},
  address = {Cham},
  author = {Navarro Bullock, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Social Information Access: Systems and Technologies},
  editor = {Brusilovsky, Peter and He, Daqing},
  keywords = {itegpub},
  pages = {310--343},
  publisher = {Springer International Publishing},
  title = {Accessing Information with Tags: Search and Ranking},
  year = 2018
  }
  

  %0 Book Section
  %1 NavarroBullock2018
  %A Navarro Bullock, Beate
  %A Hotho, Andreas
  %A Stumme, Gerd
  %B Social Information Access: Systems and Technologies
  %C Cham
  %D 2018
  %E Brusilovsky, Peter
  %E He, Daqing
  %I Springer International Publishing
  %P 310--343
  %R 10.1007/978-3-319-90092-6_9
  %T Accessing Information with Tags: Search and Ranking
  %U https://doi.org/10.1007/978-3-319-90092-6_9
  %X With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.
  %@ 978-3-319-90092-6
  

• 1.

  Felde, M., Hanika, T.: Formal Context Generation using Dirichlet Distributions. CoRR. abs/1809.11160, (2018).

  URLBibTeXEndNoteBibSonomy

  @article{journals/corr/abs-1809-11160,
  author = {Felde, Maximilian and Hanika, Tom},
  journal = {CoRR},
  keywords = {kde},
  note = {Accepted for ICCS'19},
  title = {Formal Context Generation using Dirichlet Distributions.},
  volume = {abs/1809.11160},
  year = 2018
  }
  

  %0 Journal Article
  %1 journals/corr/abs-1809-11160
  %A Felde, Maximilian
  %A Hanika, Tom
  %D 2018
  %J CoRR
  %T Formal Context Generation using Dirichlet Distributions.
  %U http://dblp.uni-trier.de/db/journals/corr/corr1809.html#abs-1809-11160
  %V abs/1809.11160
  

• 1.

  Doerfel, S., Hanika, T., Stumme, G.: Clones in Graphs. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G.A., and Ras, Z.W. (eds.) ISMIS. pp. 56–66. Springer (2018). https://doi.org/10.1007/978-3-030-01851-1_6.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/ismis/DoerfelHS18,
  author = {Doerfel, Stephan and Hanika, Tom and Stumme, Gerd},
  booktitle = {ISMIS},
  crossref = {conf/ismis/2018},
  editor = {Ceci, Michelangelo and Japkowicz, Nathalie and Liu, Jiming and Papadopoulos, George A. and Ras, Zbigniew W.},
  keywords = {kdepub},
  pages = {56-66},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Clones in Graphs.},
  volume = 11177,
  year = 2018
  }
  

  %0 Conference Paper
  %1 conf/ismis/DoerfelHS18
  %A Doerfel, Stephan
  %A Hanika, Tom
  %A Stumme, Gerd
  %B ISMIS
  %D 2018
  %E Ceci, Michelangelo
  %E Japkowicz, Nathalie
  %E Liu, Jiming
  %E Papadopoulos, George A.
  %E Ras, Zbigniew W.
  %I Springer
  %P 56-66
  %R 10.1007/978-3-030-01851-1_6
  %T Clones in Graphs.
  %U http://dblp.uni-trier.de/db/conf/ismis/ismis2018.html#DoerfelHS18
  %V 11177
  %@ 978-3-030-01851-1
  

• 1.

  Demel, A., Dürrschnabel, D., Mchedlidze, T., Radermacher, M., Wulf, L.: A Greedy Heuristic for Crossing-Angle Maximization. In: Biedl, T.C. and Kerren, A. (eds.) Graph Drawing. pp. 286–299. Springer (2018).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{conf/gd/DemelDMRW18,
  author = {Demel, Almut and Dürrschnabel, Dominik and Mchedlidze, Tamara and Radermacher, Marcel and Wulf, Lasse},
  booktitle = {Graph Drawing},
  crossref = {conf/gd/2018},
  editor = {Biedl, Therese C. and Kerren, Andreas},
  keywords = {greedy_heuristik},
  pages = {286-299},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {A Greedy Heuristic for Crossing-Angle Maximization.},
  volume = 11282,
  year = 2018
  }
  

  %0 Conference Paper
  %1 conf/gd/DemelDMRW18
  %A Demel, Almut
  %A Dürrschnabel, Dominik
  %A Mchedlidze, Tamara
  %A Radermacher, Marcel
  %A Wulf, Lasse
  %B Graph Drawing
  %D 2018
  %E Biedl, Therese C.
  %E Kerren, Andreas
  %I Springer
  %P 286-299
  %T A Greedy Heuristic for Crossing-Angle Maximization.
  %U http://dblp.uni-trier.de/db/conf/gd/gd2018.html#DemelDMRW18
  %V 11282
  %@ 978-3-030-04414-5
  

• 1.

  Thiele, L., Atzmueller, M., Stumme, G., Kauffeld, S.: Frequent and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Students’ Developmental Peer Network Relationships. Psychology. 09, 633–654 (2018). https://doi.org/10.4236/psych.2018.94040.

  URLBibTeXEndNoteDOIBibSonomy

  @article{thiele2018frequent,
  author = {Thiele, Lisa and Atzmueller, Martin and Stumme, Gerd and Kauffeld, Simone},
  journal = {Psychology},
  keywords = {itegpub},
  number = {04},
  pages = {633--654},
  publisher = {Scientific Research Publishing, Inc,},
  title = {Frequent and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Students' Developmental Peer Network Relationships},
  volume = {09},
  year = 2018
  }
  

  %0 Journal Article
  %1 thiele2018frequent
  %A Thiele, Lisa
  %A Atzmueller, Martin
  %A Stumme, Gerd
  %A Kauffeld, Simone
  %D 2018
  %I Scientific Research Publishing, Inc,
  %J Psychology
  %N 04
  %P 633--654
  %R 10.4236/psych.2018.94040
  %T Frequent and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Students' Developmental Peer Network Relationships
  %U https://doi.org/10.4236%2Fpsych.2018.94040
  %V 09
  

• 1.

  Hanika, T., Zumbrägel, J.: Towards Collaborative Conceptual Exploration. In: Chapman, P., Endres, D., and Pernelle, N. (eds.) ICCS. pp. 120–134. Springer (2018). https://doi.org/10.1007/978-3-319-91379-7_10.

  URLBibTeXEndNoteDOIBibSonomy

  @inproceedings{conf/iccs/HanikaZ18,
  author = {Hanika, Tom and Zumbrägel, Jens},
  booktitle = {ICCS},
  crossref = {conf/iccs/2018},
  editor = {Chapman, Peter and Endres, Dominik and Pernelle, Nathalie},
  keywords = {itegpub},
  pages = {120-134},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Towards Collaborative Conceptual Exploration.},
  volume = 10872,
  year = 2018
  }
  

  %0 Conference Paper
  %1 conf/iccs/HanikaZ18
  %A Hanika, Tom
  %A Zumbrägel, Jens
  %B ICCS
  %D 2018
  %E Chapman, Peter
  %E Endres, Dominik
  %E Pernelle, Nathalie
  %I Springer
  %P 120-134
  %R 10.1007/978-3-319-91379-7_10
  %T Towards Collaborative Conceptual Exploration.
  %U http://dblp.uni-trier.de/db/conf/iccs/iccs2018.html#HanikaZ18
  %V 10872
  %@ 978-3-319-91379-7
  

• 1.

  Atzmueller, M., Thiele, L., Stumme, G., Kauffeld, S.: {Analyzing Group Interaction on Networks of Face-to-Face Proximity using Wearable Sensors}. In: Proc. IEEE International Conference on Future IoT Technologies. IEEE Press, Boston, MA, USA (2018).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ATSK:18,
  address = {Boston, MA, USA},
  author = {Atzmueller, Martin and Thiele, Lisa and Stumme, Gerd and Kauffeld, Simone},
  booktitle = {Proc. IEEE International Conference on Future IoT Technologies},
  keywords = {behavior},
  publisher = {IEEE Press},
  title = {{Analyzing Group Interaction on Networks of Face-to-Face Proximity using Wearable Sensors}},
  year = 2018
  }
  

  %0 Conference Paper
  %1 ATSK:18
  %A Atzmueller, Martin
  %A Thiele, Lisa
  %A Stumme, Gerd
  %A Kauffeld, Simone
  %B Proc. IEEE International Conference on Future IoT Technologies
  %C Boston, MA, USA
  %D 2018
  %I IEEE Press
  %T {Analyzing Group Interaction on Networks of Face-to-Face Proximity using Wearable Sensors}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2018-FutureIoT-Atzmueller-DSNA.pdf
  

• 1.

  Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic dimension of concept lattices. CoRR. abs/1801.07985, (2018).

  URLBibTeXEndNoteBibSonomy

  Geometric analysis is a very capable theory to understand the influence of the high dimensionality of the input data in machine learning (ML) and knowledge discovery (KD). With our approach we can assess how far the application of a specific KD/ML-algorithm to a concrete data set is prone to the curse of dimensionality. To this end we extend V.~Pestov's axiomatic approach to the instrinsic dimension of data sets, based on the seminal work by M.~Gromov on concentration phenomena, and provide an adaptable and computationally feasible model for studying observable geometric invariants associated to features that are natural to both the data and the learning procedure. In detail, we investigate data represented by formal contexts and give first theoretical as well as experimental insights into the intrinsic dimension of a concept lattice. Because of the correspondence between formal concepts and maximal cliques in graphs, applications to social network analysis are at hand.

  @article{hanika2018intrinsic,
  abstract = {Geometric analysis is a very capable theory to understand the influence of the high dimensionality of the input data in machine learning (ML) and knowledge discovery (KD). With our approach we can assess how far the application of a specific KD/ML-algorithm to a concrete data set is prone to the curse of dimensionality. To this end we extend V.~Pestov's axiomatic approach to the instrinsic dimension of data sets, based on the seminal work by M.~Gromov on concentration phenomena, and provide an adaptable and computationally feasible model for studying observable geometric invariants associated to features that are natural to both the data and the learning procedure. In detail, we investigate data represented by formal contexts and give first theoretical as well as experimental insights into the intrinsic dimension of a concept lattice. Because of the correspondence between formal concepts and maximal cliques in graphs, applications to social network analysis are at hand.},
  author = {Hanika, Tom and Schneider, Friedrich Martin and Stumme, Gerd},
  journal = {CoRR},
  keywords = {dimension},
  note = {cite arxiv:1801.07985Comment: 13 pages, 3 figures},
  title = {Intrinsic dimension of concept lattices},
  volume = {abs/1801.07985},
  year = 2018
  }
  

  %0 Journal Article
  %1 hanika2018intrinsic
  %A Hanika, Tom
  %A Schneider, Friedrich Martin
  %A Stumme, Gerd
  %D 2018
  %J CoRR
  %T Intrinsic dimension of concept lattices
  %U http://arxiv.org/abs/1801.07985
  %V abs/1801.07985
  %X Geometric analysis is a very capable theory to understand the influence of the high dimensionality of the input data in machine learning (ML) and knowledge discovery (KD). With our approach we can assess how far the application of a specific KD/ML-algorithm to a concrete data set is prone to the curse of dimensionality. To this end we extend V.~Pestov's axiomatic approach to the instrinsic dimension of data sets, based on the seminal work by M.~Gromov on concentration phenomena, and provide an adaptable and computationally feasible model for studying observable geometric invariants associated to features that are natural to both the data and the learning procedure. In detail, we investigate data represented by formal contexts and give first theoretical as well as experimental insights into the intrinsic dimension of a concept lattice. Because of the correspondence between formal concepts and maximal cliques in graphs, applications to social network analysis are at hand.
  

2017

• 1.

  Atzmueller, M.: {Exceptional Model Mining in Ubiquitous and Social Environments}. In: Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017). Eindhoven University of Technology, Eindhoven, The Netherlands (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{Atzmueller:17,
  address = {Eindhoven, The Netherlands},
  author = {Atzmueller, Martin},
  booktitle = {Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017)},
  keywords = {hypgraphs},
  publisher = {Eindhoven University of Technology},
  title = {{Exceptional Model Mining in Ubiquitous and Social Environments}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 Atzmueller:17
  %A Atzmueller, Martin
  %B Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017)
  %C Eindhoven, The Netherlands
  %D 2017
  %I Eindhoven University of Technology
  %T {Exceptional Model Mining in Ubiquitous and Social Environments}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2017-atzmueller-bl-emm-muse-preprint.pdf
  

• 1.

  Atzmueller, M., Sternberg, E.: {Mixed-Initiative Feature Engineering Using Knowledge Graphs}. In: Proc. 9th International Conference on Knowledge Capture (K-CAP). ACM Press, New York, NY, USA (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AS:17,
  address = {New York, NY, USA},
  author = {Atzmueller, Martin and Sternberg, Eric},
  booktitle = {Proc. 9th International Conference on Knowledge Capture (K-CAP)},
  keywords = {itegpub},
  month = {(accepted)},
  publisher = {ACM Press},
  title = {{Mixed-Initiative Feature Engineering Using Knowledge Graphs}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 AS:17
  %A Atzmueller, Martin
  %A Sternberg, Eric
  %B Proc. 9th International Conference on Knowledge Capture (K-CAP)
  %C New York, NY, USA
  %D 2017
  %I ACM Press
  %T {Mixed-Initiative Feature Engineering Using Knowledge Graphs}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2017-atzmueller-kcap.pdf
  

• 1.

  Atzmueller, M., Becker, M., Molino, A., Mueller, J., Peters, J., Sirbu, A.: {Applications for Environmental Sensing in EveryAware}. In: Loreto, V., Haklay, M., Hotho, A., Servedio, V.D., Stumme, G., Tria, F., and Theunis, J. (eds.) Participatory Sensing, Opinions and Collective Awareness. Springer Verlag, Heidelberg, Germany (2017).

  URLBibTeXEndNoteBibSonomy

  @incollection{ABMMPS:16,
  address = {Heidelberg, Germany},
  author = {Atzmueller, Martin and Becker, Martin and Molino, Andrea and Mueller, Juergen and Peters, Jan and Sirbu, Alina},
  booktitle = {Participatory Sensing, Opinions and Collective Awareness},
  editor = {Loreto, Vittorio and Haklay, Muki and Hotho, Andreas and Servedio, Vito D.P. and Stumme, Gerd and Tria, Francesca and Theunis, Jan},
  keywords = {analysis},
  publisher = {Springer Verlag},
  title = {{Applications for Environmental Sensing in EveryAware}},
  year = 2017
  }
  

  %0 Book Section
  %1 ABMMPS:16
  %A Atzmueller, Martin
  %A Becker, Martin
  %A Molino, Andrea
  %A Mueller, Juergen
  %A Peters, Jan
  %A Sirbu, Alina
  %B Participatory Sensing, Opinions and Collective Awareness
  %C Heidelberg, Germany
  %D 2017
  %E Loreto, Vittorio
  %E Haklay, Muki
  %E Hotho, Andreas
  %E Servedio, Vito D.P.
  %E Stumme, Gerd
  %E Tria, Francesca
  %E Theunis, Jan
  %I Springer Verlag
  %T {Applications for Environmental Sensing in EveryAware}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2016-EveryAware-Book-part_I_ch_7_Atzmueller.pdf
  

• 1.

  Borchmann, D., Hanika, T., Obiedkov, S.: On the Usability of Probably Approximately Correct Implication Bases. CoRR. abs/1701.00877, (2017).

  URLBibTeXEndNoteBibSonomy

  @article{journals/corr/BorchmannHO17,
  author = {Borchmann, Daniel and Hanika, Tom and Obiedkov, Sergei},
  journal = {CoRR},
  keywords = {implications},
  title = {On the Usability of Probably Approximately Correct Implication Bases.},
  volume = {abs/1701.00877},
  year = 2017
  }
  

  %0 Journal Article
  %1 journals/corr/BorchmannHO17
  %A Borchmann, Daniel
  %A Hanika, Tom
  %A Obiedkov, Sergei
  %D 2017
  %J CoRR
  %T On the Usability of Probably Approximately Correct Implication Bases.
  %U http://dblp.uni-trier.de/db/journals/corr/corr1701.html#BorchmannHO17
  %V abs/1701.00877
  

• 1.

  Atzmueller, M., Becker, M., Mueller, J.: {Collective Sensing Platforms}. In: Loreto, V., Haklay, M., Hotho, A., Servedio, V.D., Stumme, G., Tria, F., and Theunis, J. (eds.) Participatory Sensing, Opinions and Collective Awareness. Springer Verlag, Heidelberg, Germany (2017).

  URLBibTeXEndNoteBibSonomy

  @incollection{ABM:16,
  address = {Heidelberg, Germany},
  author = {Atzmueller, Martin and Becker, Martin and Mueller, Juergen},
  booktitle = {Participatory Sensing, Opinions and Collective Awareness},
  editor = {Loreto, Vittorio and Haklay, Muki and Hotho, Andreas and Servedio, Vito D.P. and Stumme, Gerd and Tria, Francesca and Theunis, Jan},
  keywords = {itegpub},
  publisher = {Springer Verlag},
  title = {{Collective Sensing Platforms}},
  year = 2017
  }
  

  %0 Book Section
  %1 ABM:16
  %A Atzmueller, Martin
  %A Becker, Martin
  %A Mueller, Juergen
  %B Participatory Sensing, Opinions and Collective Awareness
  %C Heidelberg, Germany
  %D 2017
  %E Loreto, Vittorio
  %E Haklay, Muki
  %E Hotho, Andreas
  %E Servedio, Vito D.P.
  %E Stumme, Gerd
  %E Tria, Francesca
  %E Theunis, Jan
  %I Springer Verlag
  %T {Collective Sensing Platforms}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2016-EveryAware-Book-part_I_ch_6_Atzmueller.pdf
  

• 1.

  Atzmueller, M., Thiele, L., Stumme, G., Kauffeld, S.: {Contact Patterns, Group Interaction and Dynamics on Socio-Behavioral Multiplex Networks}. In: Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017). Eindhoven University of Technology, Eindhoven, The Netherlands (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ATSK:17,
  address = {Eindhoven, The Netherlands},
  author = {Atzmueller, Martin and Thiele, Lisa and Stumme, Gerd and Kauffeld, Simone},
  booktitle = {Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017)},
  keywords = {contacts},
  publisher = {Eindhoven University of Technology},
  title = {{Contact Patterns, Group Interaction and Dynamics on Socio-Behavioral Multiplex Networks}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 ATSK:17
  %A Atzmueller, Martin
  %A Thiele, Lisa
  %A Stumme, Gerd
  %A Kauffeld, Simone
  %B Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017)
  %C Eindhoven, The Netherlands
  %D 2017
  %I Eindhoven University of Technology
  %T {Contact Patterns, Group Interaction and Dynamics on Socio-Behavioral Multiplex Networks}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2017-atzmueller-bl-connectu-preprint.pdf
  

• 1.

  Atzmueller, M.: {Onto Explicative Data Mining: Exploratory, Interpretable and Explainable Analysis}. In: Proc. Dutch-Belgian Database Day. TU Eindhoven, Netherlands (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{Atzmueller:DBDBD17,
  author = {Atzmueller, Martin},
  booktitle = {Proc. Dutch-Belgian Database Day},
  keywords = {explanation-aware},
  month = {(accepted)},
  organization = {TU Eindhoven, Netherlands},
  title = {{Onto Explicative Data Mining: Exploratory, Interpretable and Explainable Analysis}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 Atzmueller:DBDBD17
  %A Atzmueller, Martin
  %B Proc. Dutch-Belgian Database Day
  %D 2017
  %T {Onto Explicative Data Mining: Exploratory, Interpretable and Explainable Analysis}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2017-atzmueller-dbdbd-preprint.pdf
  

• 1.

  Atzmueller, M., Schmidt, A., Kloepper, B., Arnu, D.: {HypGraphs: An Approach for Analysis and Assessment of Graph-Based and Sequential Hypotheses}. In: New Frontiers in Mining Complex Patterns. Postproceedings NFMCP 2016. Springer Verlag, Berlin/Heidelberg, Germany (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ASKA:17,
  address = {Berlin/Heidelberg, Germany},
  author = {Atzmueller, Martin and Schmidt, Andreas and Kloepper, Benjamin and Arnu, David},
  booktitle = {New Frontiers in Mining Complex Patterns. Postproceedings NFMCP 2016},
  keywords = {sequential},
  month = {(In Press)},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {{HypGraphs: An Approach for Analysis and Assessment of Graph-Based and Sequential Hypotheses}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 ASKA:17
  %A Atzmueller, Martin
  %A Schmidt, Andreas
  %A Kloepper, Benjamin
  %A Arnu, David
  %B New Frontiers in Mining Complex Patterns. Postproceedings NFMCP 2016
  %C Berlin/Heidelberg, Germany
  %D 2017
  %I Springer Verlag
  %T {HypGraphs: An Approach for Analysis and Assessment of Graph-Based and Sequential Hypotheses}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/Atzmueller-Hypgraphs-NFMCP2016-PP.pdf
  

• 1.

  Mueller, J., Stumme, G.: Predicting Rising Follower Counts on Twitter Using Profile Information. In: 9th International ACM Web Science Conference 2017 (WebSci 2017), Troy, NY, USA, June 26-28, 2017. Accepted for Publication. ACM, New York, NY, USA (2017). https://doi.org/10.1145/3091478.3091490.

  URLBibTeXEndNoteDOIBibSonomy

  When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.

  @inproceedings{mueller-2017,
  abstract = {When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.},
  address = {New York, NY, USA},
  author = {Mueller, Juergen and Stumme, Gerd},
  booktitle = {9th International ACM Web Science Conference 2017 (WebSci 2017), Troy, NY, USA, June 26-28, 2017. Accepted for Publication},
  keywords = {KDE},
  month = {06},
  publisher = {ACM},
  title = {Predicting Rising Follower Counts on Twitter Using Profile Information},
  year = 2017
  }
  

  %0 Conference Paper
  %1 mueller-2017
  %A Mueller, Juergen
  %A Stumme, Gerd
  %B 9th International ACM Web Science Conference 2017 (WebSci 2017), Troy, NY, USA, June 26-28, 2017. Accepted for Publication
  %C New York, NY, USA
  %D 2017
  %I ACM
  %R 10.1145/3091478.3091490
  %T Predicting Rising Follower Counts on Twitter Using Profile Information
  %U http://dx.doi.org/10.1145/3091478.3091490
  %X When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.
  %@ 978-1-4503-4896-6
  

• 1.

  Kibanov, M., Stumme, G., Amin, I., Lee, J.G.: Mining social media to inform peatland fire and haze disaster management. Social Network Analysis and Mining. 7, 30 (2017). https://doi.org/10.1007/s13278-017-0446-1.

  URLBibTeXEndNoteDOIBibSonomy

  Peatland fires and haze events are disasters with national, regional, and international implications. The phenomena lead to direct damage to local assets, as well as broader economic and environmental losses. Satellite imagery is still the main and often the only available source of information for disaster management. In this article, we test the potential of social media to assist disaster management. To this end, we compare insights from two datasets: fire hotspots detected via NASA satellite imagery and almost all GPS-stamped tweets from Sumatra Island, Indonesia, posted during 2014. Sumatra Island is chosen as it regularly experiences a significant number of haze events, which affect citizens in Indonesia as well as in nearby countries including Malaysia and Singapore. We analyze temporal correlations between the datasets and their geo-spatial interdependence. Furthermore, we show how Twitter data reveal changes in users' behavior during severe haze events. Overall, we demonstrate that social media are a valuable source of complementary and supplementary information for haze disaster management. Based on our methodology and findings, an analytics tool to improve peatland fire and haze disaster management by the Indonesian authorities is under development.

  @article{kibanov2017mining,
  abstract = {Peatland fires and haze events are disasters with national, regional, and international implications. The phenomena lead to direct damage to local assets, as well as broader economic and environmental losses. Satellite imagery is still the main and often the only available source of information for disaster management. In this article, we test the potential of social media to assist disaster management. To this end, we compare insights from two datasets: fire hotspots detected via NASA satellite imagery and almost all GPS-stamped tweets from Sumatra Island, Indonesia, posted during 2014. Sumatra Island is chosen as it regularly experiences a significant number of haze events, which affect citizens in Indonesia as well as in nearby countries including Malaysia and Singapore. We analyze temporal correlations between the datasets and their geo-spatial interdependence. Furthermore, we show how Twitter data reveal changes in users' behavior during severe haze events. Overall, we demonstrate that social media are a valuable source of complementary and supplementary information for haze disaster management. Based on our methodology and findings, an analytics tool to improve peatland fire and haze disaster management by the Indonesian authorities is under development.},
  author = {Kibanov, Mark and Stumme, Gerd and Amin, Imaduddin and Lee, Jong Gun},
  journal = {Social Network Analysis and Mining},
  keywords = 2017,
  month = {07},
  number = 1,
  pages = 30,
  title = {Mining social media to inform peatland fire and haze disaster management},
  volume = 7,
  year = 2017
  }
  

  %0 Journal Article
  %1 kibanov2017mining
  %A Kibanov, Mark
  %A Stumme, Gerd
  %A Amin, Imaduddin
  %A Lee, Jong Gun
  %D 2017
  %J Social Network Analysis and Mining
  %N 1
  %P 30
  %R 10.1007/s13278-017-0446-1
  %T Mining social media to inform peatland fire and haze disaster management
  %U http://rdcu.be/udmt
  %V 7
  %X Peatland fires and haze events are disasters with national, regional, and international implications. The phenomena lead to direct damage to local assets, as well as broader economic and environmental losses. Satellite imagery is still the main and often the only available source of information for disaster management. In this article, we test the potential of social media to assist disaster management. To this end, we compare insights from two datasets: fire hotspots detected via NASA satellite imagery and almost all GPS-stamped tweets from Sumatra Island, Indonesia, posted during 2014. Sumatra Island is chosen as it regularly experiences a significant number of haze events, which affect citizens in Indonesia as well as in nearby countries including Malaysia and Singapore. We analyze temporal correlations between the datasets and their geo-spatial interdependence. Furthermore, we show how Twitter data reveal changes in users' behavior during severe haze events. Overall, we demonstrate that social media are a valuable source of complementary and supplementary information for haze disaster management. Based on our methodology and findings, an analytics tool to improve peatland fire and haze disaster management by the Indonesian authorities is under development.
  

• 1.

  Thiele, L., Sauer, N.C., Atzmueller, M., Kauffeld, S.: {The Co-Evolution of Career Aspirations and Peer Relationships in Psychology Bachelor Students: A Longitudinal Social Network Study}. Journal of Vocational Behavior. (2017).

  URLBibTeXEndNoteBibSonomy

  @article{TSAK:2017,
  author = {Thiele, Lisa and Sauer, Nils Christian and Atzmueller, Martin and Kauffeld, Simone},
  journal = {Journal of Vocational Behavior},
  keywords = {longitudinal},
  title = {{The Co-Evolution of Career Aspirations and Peer Relationships in Psychology Bachelor Students: A Longitudinal Social Network Study}},
  year = 2017
  }
  

  %0 Journal Article
  %1 TSAK:2017
  %A Thiele, Lisa
  %A Sauer, Nils Christian
  %A Atzmueller, Martin
  %A Kauffeld, Simone
  %D 2017
  %J Journal of Vocational Behavior
  %T {The Co-Evolution of Career Aspirations and Peer Relationships in Psychology Bachelor Students: A Longitudinal Social Network Study}
  %U https://www.sciencedirect.com/science/article/abs/pii/S0001879117301665
  

• 1.

  Gautama, S., Atzmueller, M., Kostakos, V., Gillis, D., Hosio, S.: {Observing Human Activity Through Sensing}. In: Loreto, V., Haklay, M., Hotho, A., Servedio, V.D., Stumme, G., Tria, F., and Theunis, J. (eds.) Participatory Sensing, Opinions and Collective Awareness. Springer Verlag, Heidelberg, Germany (2017).

  URLBibTeXEndNoteBibSonomy

  @incollection{GAKGH:16,
  address = {Heidelberg, Germany},
  author = {Gautama, Sidharta and Atzmueller, Martin and Kostakos, Vasillis and Gillis, Dominique and Hosio, Simo},
  booktitle = {Participatory Sensing, Opinions and Collective Awareness},
  editor = {Loreto, Vittorio and Haklay, Muki and Hotho, Andreas and Servedio, Vito D.P. and Stumme, Gerd and Tria, Francesca and Theunis, Jan},
  keywords = {human},
  publisher = {Springer Verlag},
  title = {{Observing Human Activity Through Sensing}},
  year = 2017
  }
  

  %0 Book Section
  %1 GAKGH:16
  %A Gautama, Sidharta
  %A Atzmueller, Martin
  %A Kostakos, Vasillis
  %A Gillis, Dominique
  %A Hosio, Simo
  %B Participatory Sensing, Opinions and Collective Awareness
  %C Heidelberg, Germany
  %D 2017
  %E Loreto, Vittorio
  %E Haklay, Muki
  %E Hotho, Andreas
  %E Servedio, Vito D.P.
  %E Stumme, Gerd
  %E Tria, Francesca
  %E Theunis, Jan
  %I Springer Verlag
  %T {Observing Human Activity Through Sensing}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2016-EveryAware-Book-part_I_ch_3_Gautama.pdf
  

• 1.

  Atzmueller, M., Arnu, D., Schmidt, A.: {Anomaly Detection and Structural Analysis in Industrial Production Environments}. In: Proc. International Data Science Conference (IDSC 2017). , Salzburg, Austria (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AAS:17,
  address = {Salzburg, Austria},
  author = {Atzmueller, Martin and Arnu, David and Schmidt, Andreas},
  booktitle = {Proc. International Data Science Conference (IDSC 2017)},
  keywords = {feepub},
  title = {{Anomaly Detection and Structural Analysis in Industrial Production Environments}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 AAS:17
  %A Atzmueller, Martin
  %A Arnu, David
  %A Schmidt, Andreas
  %B Proc. International Data Science Conference (IDSC 2017)
  %C Salzburg, Austria
  %D 2017
  %T {Anomaly Detection and Structural Analysis in Industrial Production Environments}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2017-atzmueller-idsc-preprint.pdf
  

• 1.

  Borchmann, D., Hanika, T.: Individuality in Social Networks. In: Missaoui, R., Kuznetsov, S.O., and Obiedkov, S. (eds.) Formal Concept Analysis of Social Networks. pp. 19–40. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-64167-6_2.

  URLBibTeXEndNoteDOIBibSonomy

  We consider individuality in bi-modal social networks, a facet that has not been considered before in the mathematical analysis of social networks. We use methods from formal concept analysis to develop a natural definition for individuality, and provide experimental evidence that this yields a meaningful approach for additional insights into the nature of social networks.

  @inbook{Borchmann2017,
  abstract = {We consider individuality in bi-modal social networks, a facet that has not been considered before in the mathematical analysis of social networks. We use methods from formal concept analysis to develop a natural definition for individuality, and provide experimental evidence that this yields a meaningful approach for additional insights into the nature of social networks.},
  address = {Cham},
  author = {Borchmann, Daniel and Hanika, Tom},
  booktitle = {Formal Concept Analysis of Social Networks},
  editor = {Missaoui, Rokia and Kuznetsov, Sergei O. and Obiedkov, Sergei},
  keywords = {itegpub},
  pages = {19--40},
  publisher = {Springer International Publishing},
  title = {Individuality in Social Networks},
  year = 2017
  }
  

  %0 Book Section
  %1 Borchmann2017
  %A Borchmann, Daniel
  %A Hanika, Tom
  %B Formal Concept Analysis of Social Networks
  %C Cham
  %D 2017
  %E Missaoui, Rokia
  %E Kuznetsov, Sergei O.
  %E Obiedkov, Sergei
  %I Springer International Publishing
  %P 19--40
  %R 10.1007/978-3-319-64167-6_2
  %T Individuality in Social Networks
  %U https://doi.org/10.1007/978-3-319-64167-6_2
  %X We consider individuality in bi-modal social networks, a facet that has not been considered before in the mathematical analysis of social networks. We use methods from formal concept analysis to develop a natural definition for individuality, and provide experimental evidence that this yields a meaningful approach for additional insights into the nature of social networks.
  %@ 978-3-319-64167-6
  

• 1.

  Folmer, J., Kirchen, I., Trunzer, E., Vogel-Heuser, B., Pötter, T., Graube, M., Heinze, S., Urbas, L., Atzmueller, M., Arnu, D.: {Challenges for Big and Smart Data in Process Industries}. atp edition. 01-02, (2017).

  BibTeXEndNoteBibSonomy

  @article{SmartData:ATP:2017,
  author = {Folmer, Jens and Kirchen, Iris and Trunzer, Emanuel and Vogel-Heuser, Birgit and Pötter, Thorsten and Graube, Markus and Heinze, Sebastian and Urbas, Leon and Atzmueller, Martin and Arnu, David},
  journal = {atp edition},
  keywords = {smart},
  title = {{Challenges for Big and Smart Data in Process Industries}},
  volume = {01-02},
  year = 2017
  }
  

  %0 Journal Article
  %1 SmartData:ATP:2017
  %A Folmer, Jens
  %A Kirchen, Iris
  %A Trunzer, Emanuel
  %A Vogel-Heuser, Birgit
  %A Pötter, Thorsten
  %A Graube, Markus
  %A Heinze, Sebastian
  %A Urbas, Leon
  %A Atzmueller, Martin
  %A Arnu, David
  %D 2017
  %J atp edition
  %T {Challenges for Big and Smart Data in Process Industries}
  %V 01-02
  

• 1.

  Knoell, D., Atzmueller, M., Rieder, C., Scherer, K.P.: {A Scalable Framework for Data-Driven Ontology Evaluation}. In: Proc. GWEM 2017, co-located with 9th Conference Professional Knowledge Management (WM 2017). KIT, Karlsruhe, Germany (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{KARS:17:GWEM,
  address = {Karlsruhe, Germany},
  author = {Knoell, Daniel and Atzmueller, Martin and Rieder, Constantin and Scherer, Klaus Peter},
  booktitle = {Proc. GWEM 2017, co-located with 9th Conference Professional Knowledge Management (WM 2017)},
  keywords = {ontology},
  publisher = {KIT},
  title = {{A Scalable Framework for Data-Driven Ontology Evaluation}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 KARS:17:GWEM
  %A Knoell, Daniel
  %A Atzmueller, Martin
  %A Rieder, Constantin
  %A Scherer, Klaus Peter
  %B Proc. GWEM 2017, co-located with 9th Conference Professional Knowledge Management (WM 2017)
  %C Karlsruhe, Germany
  %D 2017
  %I KIT
  %T {A Scalable Framework for Data-Driven Ontology Evaluation}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/Knoell-GWEM2017-Preprint.pdf
  

• 1.

  Kanawati, R., Atzmueller, M.: {Mining Attributed Networks}, https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-dsaa17-abstract.pdf, (2017).

  URLBibTeXEndNoteBibSonomy

  @misc{RA:17:DSAA,
  author = {Kanawati, Rushed and Atzmueller, Martin},
  howpublished = {DSAA 2017, Tutorial Abstract},
  keywords = {itegpub},
  title = {{Mining Attributed Networks}},
  year = 2017
  }
  

  %0 Generic
  %1 RA:17:DSAA
  %A Kanawati, Rushed
  %A Atzmueller, Martin
  %D 2017
  %T {Mining Attributed Networks}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-dsaa17-abstract.pdf
  

• 1.

  Atzmueller, M.: {Community Detection and Analysis on Attributed Social Networks}. In: Encyclopedia of Social Network Analysis and Mining. Springer, Heidelberg, Germany (2017).

  URLBibTeXEndNoteBibSonomy

  @incollection{Atzmueller:17:ESNAM,
  address = {Heidelberg, Germany},
  author = {Atzmueller, Martin},
  booktitle = {Encyclopedia of Social Network Analysis and Mining},
  keywords = {iteg},
  publisher = {Springer},
  title = {{Community Detection and Analysis on Attributed Social Networks}},
  year = 2017
  }
  

  %0 Book Section
  %1 Atzmueller:17:ESNAM
  %A Atzmueller, Martin
  %B Encyclopedia of Social Network Analysis and Mining
  %C Heidelberg, Germany
  %D 2017
  %I Springer
  %T {Community Detection and Analysis on Attributed Social Networks}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-community-detection-analysis-attributed-networks-esnam-preprint.pdf
  

• 1.

  Atzmueller, M., Hayat, N., Schmidt, A., Klöpper, B.: {Explanation-Aware Feature Selection using Symbolic Time Series Abstraction: Approaches and Experiences in a Petro-Chemical Production Context}. In: Proc. IEEE International Conference on Industrial Informatics (INDIN). IEEE Press, Boston, MA, USA (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AHSK:17,
  address = {Boston, MA, USA},
  author = {Atzmueller, Martin and Hayat, Naveed and Schmidt, Andreas and Klöpper, Benjamin},
  booktitle = {Proc. IEEE International Conference on Industrial Informatics (INDIN)},
  keywords = {feepub},
  publisher = {IEEE Press},
  title = {{Explanation-Aware Feature Selection using Symbolic Time Series Abstraction: Approaches and Experiences in a Petro-Chemical Production Context}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 AHSK:17
  %A Atzmueller, Martin
  %A Hayat, Naveed
  %A Schmidt, Andreas
  %A Klöpper, Benjamin
  %B Proc. IEEE International Conference on Industrial Informatics (INDIN)
  %C Boston, MA, USA
  %D 2017
  %I IEEE Press
  %T {Explanation-Aware Feature Selection using Symbolic Time Series Abstraction: Approaches and Experiences in a Petro-Chemical Production Context}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2017-atzmueller-indin-preprint.pdf
  

• 1.

  Atzmueller, M., Arnu, D., Schmidt, A.: {Anomaly Analytics and Structural Assessment in Process Industries}. In: Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017). Eindhoven University of Technology, Eindhoven, The Netherlands (2017).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AAS:17a,
  address = {Eindhoven, The Netherlands},
  author = {Atzmueller, Martin and Arnu, David and Schmidt, Andreas},
  booktitle = {Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017)},
  keywords = {hypgraphs},
  publisher = {Eindhoven University of Technology},
  title = {{Anomaly Analytics and Structural Assessment in Process Industries}},
  year = 2017
  }
  

  %0 Conference Paper
  %1 AAS:17a
  %A Atzmueller, Martin
  %A Arnu, David
  %A Schmidt, Andreas
  %B Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017)
  %C Eindhoven, The Netherlands
  %D 2017
  %I Eindhoven University of Technology
  %T {Anomaly Analytics and Structural Assessment in Process Industries}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2017-atzmueller-bl-hypgraphs-preprint.pdf
  

• 1.

  Hanika, T., Zumbrägel, J.: Towards Collaborative Conceptual Exploration. CoRR. abs/1712.08858, (2017).

  URLBibTeXEndNoteBibSonomy

  @article{journals/corr/abs-1712-08858,
  author = {Hanika, Tom and Zumbrägel, Jens},
  journal = {CoRR},
  keywords = {kde},
  title = {Towards Collaborative Conceptual Exploration.},
  volume = {abs/1712.08858},
  year = 2017
  }
  

  %0 Journal Article
  %1 journals/corr/abs-1712-08858
  %A Hanika, Tom
  %A Zumbrägel, Jens
  %D 2017
  %J CoRR
  %T Towards Collaborative Conceptual Exploration.
  %U http://dblp.uni-trier.de/db/journals/corr/corr1712.html#abs-1712-08858
  %V abs/1712.08858
  

• 1.

  Loreto, V., Haklay, M., Hotho, A., Servedio, V.C.P., Stumme, G., Theunis, J., Tria, F. eds.: Participatory sensing, opinions and collective awareness. Springer (2017).

  BibTeXEndNoteBibSonomy

  @book{loreto2017participatory,
  editor = {Loreto, Vittorio and Haklay, Mordechai and Hotho, Andreas and Servedio, Vito C. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca},
  keywords = {myown},
  publisher = {Springer},
  title = {Participatory sensing, opinions and collective awareness},
  year = 2017
  }
  

  %0 Book
  %1 loreto2017participatory
  %D 2017
  %E Loreto, Vittorio
  %E Haklay, Mordechai
  %E Hotho, Andreas
  %E Servedio, Vito C. P.
  %E Stumme, Gerd
  %E Theunis, Jan
  %E Tria, Francesca
  %I Springer
  %T Participatory sensing, opinions and collective awareness
  %@ 9783319256580 3319256580
  

• 1.

  Atzmueller, M.: {Descriptive Community Detection}. In: Missaoui, R., Obiedkov, S., and Kuznetsov, S. (eds.) Formal Concept Analysis in Social Network Analysis. Springer Verlag, Berlin/Heidelberg, Germany (2017).

  URLBibTeXEndNoteBibSonomy

  @incollection{Atzmueller:17:SNA:DCD,
  address = {Berlin/Heidelberg, Germany},
  author = {Atzmueller, Martin},
  booktitle = {Formal Concept Analysis in Social Network Analysis},
  editor = {Missaoui, Rokia and Obiedkov, Sergei and Kuznetsov, Sergei},
  keywords = {itegpub},
  publisher = {Springer Verlag},
  title = {{Descriptive Community Detection}},
  year = 2017
  }
  

  %0 Book Section
  %1 Atzmueller:17:SNA:DCD
  %A Atzmueller, Martin
  %B Formal Concept Analysis in Social Network Analysis
  %C Berlin/Heidelberg, Germany
  %D 2017
  %E Missaoui, Rokia
  %E Obiedkov, Sergei
  %E Kuznetsov, Sergei
  %I Springer Verlag
  %T {Descriptive Community Detection}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2017-atzmueller-descriptive-community-detection.pdf
  

2016

• 1.

  Knoell, D., Atzmueller, M., Rieder, C., Scherer, K.P.: {BISHOP – Big Data Driven Self-Learning Support for High-performance Ontology Population}. In: Proc. LWA 2016 (FGWM Special Track). University of Potsdam, Potsdam, Germany (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{KARS:16:LWDA,
  address = {Potsdam, Germany},
  author = {Knoell, Daniel and Atzmueller, Martin and Rieder, Constantin and Scherer, Klaus Peter},
  booktitle = {Proc. LWA 2016 (FGWM Special Track)},
  keywords = {ontology},
  publisher = {University of Potsdam},
  title = {{BISHOP – Big Data Driven Self-Learning Support for High-performance Ontology Population}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 KARS:16:LWDA
  %A Knoell, Daniel
  %A Atzmueller, Martin
  %A Rieder, Constantin
  %A Scherer, Klaus Peter
  %B Proc. LWA 2016 (FGWM Special Track)
  %C Potsdam, Germany
  %D 2016
  %I University of Potsdam
  %T {BISHOP – Big Data Driven Self-Learning Support for High-performance Ontology Population}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/BISHOP-LWDA2016.pdf
  

• 1.

  Atzmueller, M., Schmidt, A., Kibanov, M.: {DASHTrails: An Approach for Modeling and Analysis of Distribution-Adapted Sequential Hypotheses and Trails}. In: Proc. WWW 2016 (Companion). IW3C2 / ACM (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ASK:16,
  author = {Atzmueller, Martin and Schmidt, Andreas and Kibanov, Mark},
  booktitle = {Proc. WWW 2016 (Companion)},
  keywords = {feepub},
  organization = {IW3C2 / ACM},
  title = {{DASHTrails: An Approach for Modeling and Analysis of Distribution-Adapted Sequential Hypotheses and Trails}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 ASK:16
  %A Atzmueller, Martin
  %A Schmidt, Andreas
  %A Kibanov, Mark
  %B Proc. WWW 2016 (Companion)
  %D 2016
  %T {DASHTrails: An Approach for Modeling and Analysis of Distribution-Adapted Sequential Hypotheses and Trails}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2016-atzmueller-dashtrails.pdf
  

• 1.

  Atzmueller, M.: {Advances in Exploratory Pattern Analytics on Ubiquitous Data and Social Media}. In: Michaelis, S., Piatkowski, N., and Stolpe, M. (eds.) Solving Large Scale Learning Tasks: Challenges and Algorithms. Festschrift in Honour of Prof. Dr. Katharina Morik. Springer Verlag (2016).

  URLBibTeXEndNoteBibSonomy

  @incollection{Atzmueller:16m,
  author = {Atzmueller, Martin},
  booktitle = {Solving Large Scale Learning Tasks: Challenges and Algorithms. Festschrift in Honour of Prof. Dr. Katharina Morik},
  editor = {Michaelis, Stefan and Piatkowski, Nico and Stolpe, Marco},
  keywords = 2016,
  publisher = {Springer Verlag},
  series = {LNCS},
  title = {{Advances in Exploratory Pattern Analytics on Ubiquitous Data and Social Media}},
  volume = 9580,
  year = 2016
  }
  

  %0 Book Section
  %1 Atzmueller:16m
  %A Atzmueller, Martin
  %B Solving Large Scale Learning Tasks: Challenges and Algorithms. Festschrift in Honour of Prof. Dr. Katharina Morik
  %D 2016
  %E Michaelis, Stefan
  %E Piatkowski, Nico
  %E Stolpe, Marco
  %I Springer Verlag
  %T {Advances in Exploratory Pattern Analytics on Ubiquitous Data and Social Media}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2016-atzmueller-advances-in-exploratory-pattern-analytics.pdf
  %V 9580
  

• 1.

  Klöpper, B., Dix, M., Schorer, L., Ampofo, A., Atzmueller, M., Arnu, D., Klinkenberg, R.: {Defining Software Architectures for Big Data Enabled Operator Support Systems}. In: Proc. IEEE International Conference on Industrial Informatics (INDIN). IEEE Press, Boston, MA, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{KloepperEtAl:INDIN:2016,
  address = {Boston, MA, USA},
  author = {Klöpper, Benjamin and Dix, Marcel and Schorer, Lukas and Ampofo, Ann and Atzmueller, Martin and Arnu, David and Klinkenberg, Ralf},
  booktitle = {Proc. IEEE International Conference on Industrial Informatics (INDIN)},
  keywords = {itegpub},
  publisher = {IEEE Press},
  title = {{Defining Software Architectures for Big Data Enabled Operator Support Systems}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 KloepperEtAl:INDIN:2016
  %A Klöpper, Benjamin
  %A Dix, Marcel
  %A Schorer, Lukas
  %A Ampofo, Ann
  %A Atzmueller, Martin
  %A Arnu, David
  %A Klinkenberg, Ralf
  %B Proc. IEEE International Conference on Industrial Informatics (INDIN)
  %C Boston, MA, USA
  %D 2016
  %I IEEE Press
  %T {Defining Software Architectures for Big Data Enabled Operator Support Systems}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/DefiningSoftwareArchitecturesForBigDataEnabledOSS_INDIN2016Preprint.pdf
  

• 1.

  Atzmueller, M., Fries, B., Hayat, N.: {Sensing, Processing and Analytics - Augmenting the Ubicon Platform for Anticipatory Ubiquitous Computing}. In: Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. ACM Press, New York, NY, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AFH:16,
  address = {New York, NY, USA},
  author = {Atzmueller, Martin and Fries, Björn and Hayat, Naveed},
  booktitle = {Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication},
  keywords = {ubicon},
  publisher = {ACM Press},
  series = {UbiComp '16 Adjunct},
  title = {{Sensing, Processing and Analytics - Augmenting the Ubicon Platform for Anticipatory Ubiquitous Computing}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 AFH:16
  %A Atzmueller, Martin
  %A Fries, Björn
  %A Hayat, Naveed
  %B Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication
  %C New York, NY, USA
  %D 2016
  %I ACM Press
  %T {Sensing, Processing and Analytics - Augmenting the Ubicon Platform for Anticipatory Ubiquitous Computing}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-ubicon-ubicomp16.pdf
  

• 1.

  Atzmueller, M.: {Detecting Community Patterns Capturing Exceptional Link Trails}. In: Proc. IEEE/ACM ASONAM. IEEE Press, Boston, MA, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{Atzmueller:16:ASONAM,
  address = {Boston, MA, USA},
  author = {Atzmueller, Martin},
  booktitle = {Proc. IEEE/ACM ASONAM},
  keywords = {model},
  publisher = {IEEE Press},
  title = {{Detecting Community Patterns Capturing Exceptional Link Trails}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 Atzmueller:16:ASONAM
  %A Atzmueller, Martin
  %B Proc. IEEE/ACM ASONAM
  %C Boston, MA, USA
  %D 2016
  %I IEEE Press
  %T {Detecting Community Patterns Capturing Exceptional Link Trails}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-xtrails-asonam2016-preprint.pdf
  

• 1.

  Gebhardt, J., Froese, T., Krüger, A., Appel, J., Benner, R., Hammer, M., Altermann, A., Hochrein, T., Kugler, C., Jatzkowski, P., Gloy, Y.-S., Saggiomo, M., Roth, R., Elixmann, I., Tapken, H., Weber, W., Atzmueller, M., Garcke, J., Pielmeier, J., Rosen, R., Tercan, H.: {Statusreport: Chancen mit Big Data -- Best Practice}. VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (2016).

  URLBibTeXEndNoteBibSonomy

  @techreport{VDI:Statusreport:2016,
  author = {Gebhardt, Jörg and Froese, Thomas and Krüger, Andreas and Appel, Jörg and Benner, Raphael and Hammer, Markus and Altermann, Alexandra and Hochrein, Thomas and Kugler, Christoph and Jatzkowski, Phillip and Gloy, Yves-Simon and Saggiomo, Marco and Roth, Rolf and Elixmann, Inga and Tapken, Heiko and Weber, Wolfgang and Atzmueller, Martin and Garcke, Jochen and Pielmeier, Julia and Rosen, Roland and Tercan, Hasan},
  institution = {VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik},
  keywords = {itegpub},
  title = {{Statusreport: Chancen mit Big Data -- Best Practice}},
  year = 2016
  }
  

  %0 Report
  %1 VDI:Statusreport:2016
  %A Gebhardt, Jörg
  %A Froese, Thomas
  %A Krüger, Andreas
  %A Appel, Jörg
  %A Benner, Raphael
  %A Hammer, Markus
  %A Altermann, Alexandra
  %A Hochrein, Thomas
  %A Kugler, Christoph
  %A Jatzkowski, Phillip
  %A Gloy, Yves-Simon
  %A Saggiomo, Marco
  %A Roth, Rolf
  %A Elixmann, Inga
  %A Tapken, Heiko
  %A Weber, Wolfgang
  %A Atzmueller, Martin
  %A Garcke, Jochen
  %A Pielmeier, Julia
  %A Rosen, Roland
  %A Tercan, Hasan
  %D 2016
  %T {Statusreport: Chancen mit Big Data -- Best Practice}
  %U https://m.vdi.de/fileadmin/vdi_de/redakteur_dateien/gma_dateien/z01_NEU_Statusreport_Best_Practice_WEB.pdf
  

• 1.

  Mueller, J., Stumme, G.: Gender Inference using Statistical Name Characteristics in Twitter. In: 5th ASE International Conference on Social Informatics (SocInfo 2016), Union, NJ, USA, August 15-17, 2016. Proceedings. pp. 47:1–47:8. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2955129.2955182.

  URLBibTeXEndNoteDOIBibSonomy

  Much attention has been given to the task of gender inference of Twitter users. Although names are strong gender indicators, the names of Twitter users are rarely used as a feature; probably due to the high number of ill-formed names, which cannot be found in any name dictionary. Instead of relying solely on a name database, we propose a novel name classifier. Our approach extracts characteristics from the user names and uses those in order to assign the names to a gender. This enables us to classify international first names as well as ill-formed names.

  @inproceedings{mueller-2016,
  abstract = {Much attention has been given to the task of gender inference of Twitter users. Although names are strong gender indicators, the names of Twitter users are rarely used as a feature; probably due to the high number of ill-formed names, which cannot be found in any name dictionary. Instead of relying solely on a name database, we propose a novel name classifier. Our approach extracts characteristics from the user names and uses those in order to assign the names to a gender. This enables us to classify international first names as well as ill-formed names.},
  address = {New York, NY, USA},
  author = {Mueller, Juergen and Stumme, Gerd},
  booktitle = {5th ASE International Conference on Social Informatics (SocInfo 2016), Union, NJ, USA, August 15-17, 2016. Proceedings},
  keywords = {arXiv},
  month = {08},
  pages = {47:1--47:8},
  publisher = {ACM},
  title = {Gender Inference using Statistical Name Characteristics in Twitter},
  year = 2016
  }
  

  %0 Conference Paper
  %1 mueller-2016
  %A Mueller, Juergen
  %A Stumme, Gerd
  %B 5th ASE International Conference on Social Informatics (SocInfo 2016), Union, NJ, USA, August 15-17, 2016. Proceedings
  %C New York, NY, USA
  %D 2016
  %I ACM
  %P 47:1--47:8
  %R 10.1145/2955129.2955182
  %T Gender Inference using Statistical Name Characteristics in Twitter
  %U http://dx.doi.org/10.1145/2955129.2955182
  %X Much attention has been given to the task of gender inference of Twitter users. Although names are strong gender indicators, the names of Twitter users are rarely used as a feature; probably due to the high number of ill-formed names, which cannot be found in any name dictionary. Instead of relying solely on a name database, we propose a novel name classifier. Our approach extracts characteristics from the user names and uses those in order to assign the names to a gender. This enables us to classify international first names as well as ill-formed names.
  %@ 978-1-4503-4129-5
  

• 1.

  Doerfel, S., Jäschke, R., Stumme, G.: The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems. ACM Transactions on Intelligent Systems and Technology. 7, 40:1–40:33 (2016).

  URLBibTeXEndNoteBibSonomy

  Social bookmarking systems have established themselves as an important part in today’s web. In such systems, tag recommender systems support users during the posting of a resource by suggesting suitable tags. Tag recommender algorithms have often been evaluated in offline benchmarking experiments. Yet, the particular setup of such experiments has rarely been analyzed. In particular, since the recommendation quality usually suffers from difficulties like the sparsity of the data or the cold start problem for new resources or users, datasets have often been pruned to so-called cores (specific subsets of the original datasets) – however without much consideration of the implications on the benchmarking results. In this paper, we generalize the notion of a core by introducing the new notion of a set-core – which is independent of any graph structure – to overcome a structural drawback in the previous constructions of cores on tagging data. We show that problems caused by some types of cores can be eliminated using setcores. Further, we present a thorough analysis of tag recommender benchmarking setups using cores. To that end, we conduct a large-scale experiment on four real-world datasets in which we analyze the influence of different cores on the evaluation of recommendation algorithms. We can show that the results of the comparison of different recommendation approaches depends on the selection of core type and level. For the benchmarking of tag recommender algorithms, our results suggest that the evaluation must be set up more carefully and should not be based on one arbitrarily chosen core type and level.

  @article{doerfel2016cores,
  abstract = {Social bookmarking systems have established themselves as an important part in today’s web. In such systems, tag recommender systems support users during the posting of a resource by suggesting suitable tags. Tag recommender algorithms have often been evaluated in offline benchmarking experiments. Yet, the particular setup of such experiments has rarely been analyzed. In particular, since the recommendation quality usually suffers from difficulties like the sparsity of the data or the cold start problem for new resources or users, datasets have often been pruned to so-called cores (specific subsets of the original datasets) – however without much consideration of the implications on the benchmarking results. In this paper, we generalize the notion of a core by introducing the new notion of a set-core – which is independent of any graph structure – to overcome a structural drawback in the previous constructions of cores on tagging data. We show that problems caused by some types of cores can be eliminated using setcores. Further, we present a thorough analysis of tag recommender benchmarking setups using cores. To that end, we conduct a large-scale experiment on four real-world datasets in which we analyze the influence of different cores on the evaluation of recommendation algorithms. We can show that the results of the comparison of different recommendation approaches depends on the selection of core type and level. For the benchmarking of tag recommender algorithms, our results suggest that the evaluation must be set up more carefully and should not be based on one arbitrarily chosen core type and level.},
  author = {Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd},
  journal = {ACM Transactions on Intelligent Systems and Technology},
  keywords = {homepage},
  month = {02},
  number = 3,
  pages = {40:1-40:33},
  title = {The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems},
  volume = 7,
  year = 2016
  }
  

  %0 Journal Article
  %1 doerfel2016cores
  %A Doerfel, Stephan
  %A Jäschke, Robert
  %A Stumme, Gerd
  %D 2016
  %J ACM Transactions on Intelligent Systems and Technology
  %N 3
  %P 40:1-40:33
  %T The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems
  %U http://doi.acm.org/10.1145/2700485
  %V 7
  %X Social bookmarking systems have established themselves as an important part in today’s web. In such systems, tag recommender systems support users during the posting of a resource by suggesting suitable tags. Tag recommender algorithms have often been evaluated in offline benchmarking experiments. Yet, the particular setup of such experiments has rarely been analyzed. In particular, since the recommendation quality usually suffers from difficulties like the sparsity of the data or the cold start problem for new resources or users, datasets have often been pruned to so-called cores (specific subsets of the original datasets) – however without much consideration of the implications on the benchmarking results. In this paper, we generalize the notion of a core by introducing the new notion of a set-core – which is independent of any graph structure – to overcome a structural drawback in the previous constructions of cores on tagging data. We show that problems caused by some types of cores can be eliminated using setcores. Further, we present a thorough analysis of tag recommender benchmarking setups using cores. To that end, we conduct a large-scale experiment on four real-world datasets in which we analyze the influence of different cores on the evaluation of recommendation algorithms. We can show that the results of the comparison of different recommendation approaches depends on the selection of core type and level. For the benchmarking of tag recommender algorithms, our results suggest that the evaluation must be set up more carefully and should not be based on one arbitrarily chosen core type and level.
  

• 1.

  Atzmueller, M., Doerfel, S., Mitzlaff, F.: Description-Oriented Community Detection using Exhaustive Subgroup Discovery. Information Sciences. 329, 965–984 (2016). https://doi.org/http://dx.doi.org/10.1016/j.ins.2015.05.008.

  URLBibTeXEndNoteDOIBibSonomy

  Abstract Communities can intuitively be defined as subsets of nodes of a graph with a dense structure in the corresponding subgraph. However, for mining such communities usually only structural aspects are taken into account. Typically, no concise nor easily interpretable community description is provided. For tackling this issue, this paper focuses on description-oriented community detection using subgroup discovery. In order to provide both structurally valid and interpretable communities we utilize the graph structure as well as additional descriptive features of the graph’s nodes. A descriptive community pattern built upon these features then describes and identifies a community, i.e., a set of nodes, and vice versa. Essentially, we mine patterns in the “description space” characterizing interesting sets of nodes (i.e., subgroups) in the “graph space”; the interestingness of a community is evaluated by a selectable quality measure. We aim at identifying communities according to standard community quality measures, while providing characteristic descriptions of these communities at the same time. For this task, we propose several optimistic estimates of standard community quality functions to be used for efficient pruning of the search space in an exhaustive branch-and-bound algorithm. We demonstrate our approach in an evaluation using five real-world data sets, obtained from three different social media applications.

  @article{atzmueller2015descriptionoriented,
  abstract = {Abstract Communities can intuitively be defined as subsets of nodes of a graph with a dense structure in the corresponding subgraph. However, for mining such communities usually only structural aspects are taken into account. Typically, no concise nor easily interpretable community description is provided. For tackling this issue, this paper focuses on description-oriented community detection using subgroup discovery. In order to provide both structurally valid and interpretable communities we utilize the graph structure as well as additional descriptive features of the graph’s nodes. A descriptive community pattern built upon these features then describes and identifies a community, i.e., a set of nodes, and vice versa. Essentially, we mine patterns in the “description space” characterizing interesting sets of nodes (i.e., subgroups) in the “graph space”; the interestingness of a community is evaluated by a selectable quality measure. We aim at identifying communities according to standard community quality measures, while providing characteristic descriptions of these communities at the same time. For this task, we propose several optimistic estimates of standard community quality functions to be used for efficient pruning of the search space in an exhaustive branch-and-bound algorithm. We demonstrate our approach in an evaluation using five real-world data sets, obtained from three different social media applications.},
  author = {Atzmueller, Martin and Doerfel, Stephan and Mitzlaff, Folke},
  journal = {Information Sciences},
  keywords = {inpress},
  month = {02},
  pages = {965-984},
  title = {Description-Oriented Community Detection using Exhaustive Subgroup Discovery},
  volume = 329,
  year = 2016
  }
  

  %0 Journal Article
  %1 atzmueller2015descriptionoriented
  %A Atzmueller, Martin
  %A Doerfel, Stephan
  %A Mitzlaff, Folke
  %D 2016
  %J Information Sciences
  %P 965-984
  %R http://dx.doi.org/10.1016/j.ins.2015.05.008
  %T Description-Oriented Community Detection using Exhaustive Subgroup Discovery
  %U http://www.sciencedirect.com/science/article/pii/S0020025515003667
  %V 329
  %X Abstract Communities can intuitively be defined as subsets of nodes of a graph with a dense structure in the corresponding subgraph. However, for mining such communities usually only structural aspects are taken into account. Typically, no concise nor easily interpretable community description is provided. For tackling this issue, this paper focuses on description-oriented community detection using subgroup discovery. In order to provide both structurally valid and interpretable communities we utilize the graph structure as well as additional descriptive features of the graph’s nodes. A descriptive community pattern built upon these features then describes and identifies a community, i.e., a set of nodes, and vice versa. Essentially, we mine patterns in the “description space” characterizing interesting sets of nodes (i.e., subgroups) in the “graph space”; the interestingness of a community is evaluated by a selectable quality measure. We aim at identifying communities according to standard community quality measures, while providing characteristic descriptions of these communities at the same time. For this task, we propose several optimistic estimates of standard community quality functions to be used for efficient pruning of the search space in an exhaustive branch-and-bound algorithm. We demonstrate our approach in an evaluation using five real-world data sets, obtained from three different social media applications.
  

• 1.

  Doerfel, S., Zoller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy. ACM Transactions on the Web. 10, 14:1–14:32 (2016). https://doi.org/10.1145/2896821.

  URLBibTeXEndNoteDOIBibSonomy

  Social tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.

  @article{doerfel2016users,
  abstract = {Social tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.},
  author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus},
  journal = {ACM Transactions on the Web},
  keywords = {equality},
  month = {05},
  number = 2,
  pages = {14:1--14:32},
  publisher = {ACM},
  title = {What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy},
  volume = 10,
  year = 2016
  }
  

  %0 Journal Article
  %1 doerfel2016users
  %A Doerfel, Stephan
  %A Zoller, Daniel
  %A Singer, Philipp
  %A Niebler, Thomas
  %A Hotho, Andreas
  %A Strohmaier, Markus
  %D 2016
  %I ACM
  %J ACM Transactions on the Web
  %N 2
  %P 14:1--14:32
  %R 10.1145/2896821
  %T What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy
  %U http://dl.acm.org/citation.cfm?id=2896821
  %V 10
  %X Social tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.
  

• 1.

  Borchmann, D., Hanika, T.: Some Experimental Results on Randomly Generating Formal Contexts. In: Huchard, M. and Kuznetsov, S. (eds.) CLA. pp. 57–69. CEUR-WS.org (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{conf/cla/BorchmannH16,
  author = {Borchmann, Daniel and Hanika, Tom},
  booktitle = {CLA},
  crossref = {conf/cla/2016},
  editor = {Huchard, Marianne and Kuznetsov, Sergei},
  keywords = {itegpub},
  pages = {57-69},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {Some Experimental Results on Randomly Generating Formal Contexts.},
  volume = 1624,
  year = 2016
  }
  

  %0 Conference Paper
  %1 conf/cla/BorchmannH16
  %A Borchmann, Daniel
  %A Hanika, Tom
  %B CLA
  %D 2016
  %E Huchard, Marianne
  %E Kuznetsov, Sergei
  %I CEUR-WS.org
  %P 57-69
  %T Some Experimental Results on Randomly Generating Formal Contexts.
  %U http://dblp.uni-trier.de/db/conf/cla/cla2016.html#BorchmannH16
  %V 1624
  

• 1.

  Zoller, D., Doerfel, S., Jäschke, R., Stumme, G., Hotho, A.: Posted, visited, exported: Altmetrics in the social tagging system BibSonomy. Journal of Informetrics. 10, 732–749 (2016). https://doi.org/http://dx.doi.org/10.1016/j.joi.2016.03.005.

  URLBibTeXEndNoteDOIBibSonomy

  Abstract In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.

  @article{zoller2016posted,
  abstract = {Abstract In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.},
  author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas},
  journal = {Journal of Informetrics},
  keywords = {posted},
  number = 3,
  pages = {732 - 749},
  title = {Posted, visited, exported: Altmetrics in the social tagging system BibSonomy},
  volume = 10,
  year = 2016
  }
  

  %0 Journal Article
  %1 zoller2016posted
  %A Zoller, Daniel
  %A Doerfel, Stephan
  %A Jäschke, Robert
  %A Stumme, Gerd
  %A Hotho, Andreas
  %D 2016
  %J Journal of Informetrics
  %N 3
  %P 732 - 749
  %R http://dx.doi.org/10.1016/j.joi.2016.03.005
  %T Posted, visited, exported: Altmetrics in the social tagging system BibSonomy
  %U http://www.sciencedirect.com/science/article/pii/S1751157715300936
  %V 10
  %X Abstract In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.
  

• 1.

  Atzmueller, M., Mollenhauer, D., Schmidt, A.: {Big Data Analytics Using Local Exceptionality Detection}. In: {Enterprise Big Data Engineering, Analytics, and Management}. IGI Global, Hershey, PA, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @incollection{AMS:16,
  address = {Hershey, PA, USA},
  author = {Atzmueller, Martin and Mollenhauer, Dennis and Schmidt, Andreas},
  booktitle = {{Enterprise Big Data Engineering, Analytics, and Management}},
  keywords = {big},
  publisher = {IGI Global},
  title = {{Big Data Analytics Using Local Exceptionality Detection}},
  year = 2016
  }
  

  %0 Book Section
  %1 AMS:16
  %A Atzmueller, Martin
  %A Mollenhauer, Dennis
  %A Schmidt, Andreas
  %B {Enterprise Big Data Engineering, Analytics, and Management}
  %C Hershey, PA, USA
  %D 2016
  %I IGI Global
  %T {Big Data Analytics Using Local Exceptionality Detection}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/Atzmueller-Preprint-Big-Data-Analytics-Using-Local-Exceptionality-Detection.pdf
  

• 1.

  Atzmueller, M., Hanika, T., Stumme, G., Schaller, R., Ludwig, B.: {Social Event Network Analysis: Structure, Preferences, and Reality}. In: Proc. IEEE/ACM ASONAM. IEEE Press, Boston, MA, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AHSSL:16:ASONAM,
  address = {Boston, MA, USA},
  author = {Atzmueller, Martin and Hanika, Tom and Stumme, Gerd and Schaller, Richard and Ludwig, Bernd},
  booktitle = {Proc. IEEE/ACM ASONAM},
  keywords = {spatial},
  publisher = {IEEE Press},
  title = {{Social Event Network Analysis: Structure, Preferences, and Reality}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 AHSSL:16:ASONAM
  %A Atzmueller, Martin
  %A Hanika, Tom
  %A Stumme, Gerd
  %A Schaller, Richard
  %A Ludwig, Bernd
  %B Proc. IEEE/ACM ASONAM
  %C Boston, MA, USA
  %D 2016
  %I IEEE Press
  %T {Social Event Network Analysis: Structure, Preferences, and Reality}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-social-event-analysis-asonam16-preprint.pdf
  

• 1.

  Niebler, T., Becker, M., Zoller, D., Doerfel, S., Hotho, A.: FolkTrails: Interpreting Navigation Behavior in a Social Tagging System. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, New York, NY, USA (2016).

  URLBibTeXEndNoteBibSonomy

  Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.

  @inproceedings{niebler2016folktrails,
  abstract = {Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.},
  address = {New York, NY, USA},
  author = {Niebler, Thomas and Becker, Martin and Zoller, Daniel and Doerfel, Stephan and Hotho, Andreas},
  booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management},
  keywords = {bibsonomy},
  note = {forthcoming},
  publisher = {ACM},
  series = {CIKM '16},
  title = {FolkTrails: Interpreting Navigation Behavior in a Social Tagging System},
  year = 2016
  }
  

  %0 Conference Paper
  %1 niebler2016folktrails
  %A Niebler, Thomas
  %A Becker, Martin
  %A Zoller, Daniel
  %A Doerfel, Stephan
  %A Hotho, Andreas
  %B Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
  %C New York, NY, USA
  %D 2016
  %I ACM
  %T FolkTrails: Interpreting Navigation Behavior in a Social Tagging System
  %U http://dx.doi.org/10.1145/2983323.2983686
  %X Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.
  

• 1.

  Atzmueller, M., Oussena, S., Roth-Berghofer, T. eds.: {Enterprise Big Data Engineering, Analytics and Management}. IGI Global, Hershey, PA, USA (2016).

  BibTeXEndNoteBibSonomy

  @book{AOR:16,
  address = {Hershey, PA, USA},
  editor = {Atzmueller, Martin and Oussena, Samia and Roth-Berghofer, Thomas},
  keywords = {big},
  publisher = {IGI Global},
  title = {{Enterprise Big Data Engineering, Analytics and Management}},
  year = 2016
  }
  

  %0 Book
  %1 AOR:16
  %C Hershey, PA, USA
  %D 2016
  %E Atzmueller, Martin
  %E Oussena, Samia
  %E Roth-Berghofer, Thomas
  %I IGI Global
  %T {Enterprise Big Data Engineering, Analytics and Management}
  

• 1.

  Atzmueller, M.: {Local Exceptionality Detection on Social Interaction Networks}. In: Proc. ECML-PKDD 2016: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Springer Verlag, Heidelberg, Germany (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{Atzmueller:16,
  address = {Heidelberg, Germany},
  author = {Atzmueller, Martin},
  booktitle = {Proc. ECML-PKDD 2016: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  keywords = {interaction},
  publisher = {Springer Verlag},
  title = {{Local Exceptionality Detection on Social Interaction Networks}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 Atzmueller:16
  %A Atzmueller, Martin
  %B Proc. ECML-PKDD 2016: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
  %C Heidelberg, Germany
  %D 2016
  %I Springer Verlag
  %T {Local Exceptionality Detection on Social Interaction Networks}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-local-exceptionality-detection-ecml-pkdd-2016.pdf
  

• 1.

  Atzmueller, M., Thiele, L., Stumme, G., Kauffeld, S.: {Analyzing Group Interaction and Dynamics on Socio-Behavioral Networks of Face-to-Face Proximity}. In: Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. ACM Press, New York, NY, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ATSK:16,
  address = {New York, NY, USA},
  author = {Atzmueller, Martin and Thiele, Lisa and Stumme, Gerd and Kauffeld, Simone},
  booktitle = {Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication},
  keywords = {behavior},
  publisher = {ACM Press},
  series = {UbiComp '16 Adjunct},
  title = {{Analyzing Group Interaction and Dynamics on Socio-Behavioral Networks of Face-to-Face Proximity}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 ATSK:16
  %A Atzmueller, Martin
  %A Thiele, Lisa
  %A Stumme, Gerd
  %A Kauffeld, Simone
  %B Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication
  %C New York, NY, USA
  %D 2016
  %I ACM Press
  %T {Analyzing Group Interaction and Dynamics on Socio-Behavioral Networks of Face-to-Face Proximity}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-analyzing-group-interaction-ubicomp16.pdf
  

• 1.

  Atzmueller, M., Ernst, A., Krebs, F., Scholz, C., Stumme, G.: {Formation and Temporal Evolution of Social Groups During Coffee Breaks}. In: Postproceedings of the International Workshops MUSE & SenseML 2014, Nancy, France, and MSM 2014, Seoul, Korea. Springer Verlag, Heidelberg, Germany (2016).

  URLBibTeXEndNoteBibSonomy

  @incollection{AEKSS:15,
  address = {Heidelberg, Germany},
  author = {Atzmueller, Martin and Ernst, Andreas and Krebs, Friedrich and Scholz, Christoph and Stumme, Gerd},
  booktitle = {Postproceedings of the International Workshops MUSE & SenseML 2014, Nancy, France, and MSM 2014, Seoul, Korea},
  keywords = {group},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {{Formation and Temporal Evolution of Social Groups During Coffee Breaks}},
  year = 2016
  }
  

  %0 Book Section
  %1 AEKSS:15
  %A Atzmueller, Martin
  %A Ernst, Andreas
  %A Krebs, Friedrich
  %A Scholz, Christoph
  %A Stumme, Gerd
  %B Postproceedings of the International Workshops MUSE & SenseML 2014, Nancy, France, and MSM 2014, Seoul, Korea
  %C Heidelberg, Germany
  %D 2016
  %I Springer Verlag
  %T {Formation and Temporal Evolution of Social Groups During Coffee Breaks}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-atzmueller-group-formation-temporal-evolution.pdf
  

• 1.

  Atzmueller, M.: {Community Detection: From Plain to Attributed Complex Networks}. In: Proc. ACM Web Science Conference. ACM Press, New York, NY, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{Atzmueller:16:websci,
  address = {New York, NY, USA},
  author = {Atzmueller, Martin},
  booktitle = {Proc. ACM Web Science Conference},
  keywords = 2016,
  organization = {ACM},
  publisher = {ACM Press},
  title = {{Community Detection: From Plain to Attributed Complex Networks}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 Atzmueller:16:websci
  %A Atzmueller, Martin
  %B Proc. ACM Web Science Conference
  %C New York, NY, USA
  %D 2016
  %I ACM Press
  %T {Community Detection: From Plain to Attributed Complex Networks}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2016-atzmueller-websci-community-detection-on-complex-attributed-networks.pdf
  

• 1.

  Atzmueller, M., Schmidt, A., Kloepper, B., Arnu, D.: {HypGraphs: An Approach for Modeling and Comparing Graph-Based and Sequential Hypotheses}. In: Proc. ECML-PKDD Workshop on New Frontiers in Mining Complex Patterns (NFMCP). , Riva del Garda, Italy (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ASKA:16,
  address = {Riva del Garda, Italy},
  author = {Atzmueller, Martin and Schmidt, Andreas and Kloepper, Benjamin and Arnu, David},
  booktitle = {Proc. ECML-PKDD Workshop on New Frontiers in Mining Complex Patterns (NFMCP)},
  keywords = {feepub},
  title = {{HypGraphs: An Approach for Modeling and Comparing Graph-Based and Sequential Hypotheses}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 ASKA:16
  %A Atzmueller, Martin
  %A Schmidt, Andreas
  %A Kloepper, Benjamin
  %A Arnu, David
  %B Proc. ECML-PKDD Workshop on New Frontiers in Mining Complex Patterns (NFMCP)
  %C Riva del Garda, Italy
  %D 2016
  %T {HypGraphs: An Approach for Modeling and Comparing Graph-Based and Sequential Hypotheses}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2016-atzmueller-hypgraphs.pdf
  

• 1.

  Atzmueller, M., Chin, A., Janssen, F., Schweizer, I., Trattner, C. eds.: {Big Data Analytics in the Social and Ubiquitous Context}. Springer Verlag, Heidelberg, Germany (2016).

  BibTeXEndNoteBibSonomy

  @proceedings{ACJST:16,
  address = {Heidelberg, Germany},
  booktitle = {MSM/MUSE Postproceedings 2015},
  editor = {Atzmueller, Martin and Chin, Alvin and Janssen, Frederik and Schweizer, Immanuel and Trattner, Christoph},
  keywords = {big},
  publisher = {Springer Verlag},
  series = {Lecture Notes in Computer Science},
  title = {{Big Data Analytics in the Social and Ubiquitous Context}},
  volume = 9546,
  year = 2016
  }
  

  %0 Conference Proceedings
  %1 ACJST:16
  %B MSM/MUSE Postproceedings 2015
  %C Heidelberg, Germany
  %D 2016
  %E Atzmueller, Martin
  %E Chin, Alvin
  %E Janssen, Frederik
  %E Schweizer, Immanuel
  %E Trattner, Christoph
  %I Springer Verlag
  %T {Big Data Analytics in the Social and Ubiquitous Context}
  %V 9546
  

• 1.

  Lemmerich, F., Atzmueller, M., Puppe, F.: {Fast Exhaustive Subgroup Discovery with Numerical Target Concepts}. Data Mining and Knowledge Discovery. 30, 711–762 (2016).

  URLBibTeXEndNoteBibSonomy

  @article{LAP:15,
  author = {Lemmerich, Florian and Atzmueller, Martin and Puppe, Frank},
  journal = {Data Mining and Knowledge Discovery},
  keywords = {itegpub},
  pages = {711-762},
  title = {{Fast Exhaustive Subgroup Discovery with Numerical Target Concepts}},
  volume = 30,
  year = 2016
  }
  

  %0 Journal Article
  %1 LAP:15
  %A Lemmerich, Florian
  %A Atzmueller, Martin
  %A Puppe, Frank
  %D 2016
  %J Data Mining and Knowledge Discovery
  %P 711-762
  %T {Fast Exhaustive Subgroup Discovery with Numerical Target Concepts}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-dmkd-preprint-sdnum.pdf
  %V 30
  

• 1.

  Schmidt, A., Atzmueller, M., Hollender, M.: {Data Preparation for Big Data Analytics: Methods \& Experiences}. In: {Enterprise Big Data Engineering, Analytics, and Management}. IGI Global, Hershey, PA, USA (2016).

  URLBibTeXEndNoteBibSonomy

  @incollection{ASH:16,
  address = {Hershey, PA, USA},
  author = {Schmidt, Andreas and Atzmueller, Martin and Hollender, Martin},
  booktitle = {{Enterprise Big Data Engineering, Analytics, and Management}},
  keywords = {big},
  publisher = {IGI Global},
  title = {{Data Preparation for Big Data Analytics: Methods \& Experiences}},
  year = 2016
  }
  

  %0 Book Section
  %1 ASH:16
  %A Schmidt, Andreas
  %A Atzmueller, Martin
  %A Hollender, Martin
  %B {Enterprise Big Data Engineering, Analytics, and Management}
  %C Hershey, PA, USA
  %D 2016
  %I IGI Global
  %T {Data Preparation for Big Data Analytics: Methods \& Experiences}
  %U http://www.igi-global.com/chapter/data-preparation-for-big-data-analytics/154561
  

• 1.

  Atzmueller, M., Kloepper, B., Mawla, H.A., Jäschke, B., Hollender, M., Graube, M., Arnu, D., Schmidt, A., Heinze, S., Schorer, L., Kroll, A., Stumme, G., Urbas, L.: {Big Data Analytics for Proactive Industrial Decision Support: Approaches \& First Experiences in the Context of the FEE Project}. atp edition. 58, (2016).

  URLBibTeXEndNoteBibSonomy

  @article{FEE:ATP:2016,
  author = {Atzmueller, Martin and Kloepper, Benjamin and Mawla, Hassan Al and Jäschke, Benjamin and Hollender, Martin and Graube, Markus and Arnu, David and Schmidt, Andreas and Heinze, Sebastian and Schorer, Lukas and Kroll, Andreas and Stumme, Gerd and Urbas, Leon},
  journal = {atp edition},
  keywords = {big},
  number = 9,
  title = {{Big Data Analytics for Proactive Industrial Decision Support: Approaches \& First Experiences in the Context of the FEE Project}},
  volume = 58,
  year = 2016
  }
  

  %0 Journal Article
  %1 FEE:ATP:2016
  %A Atzmueller, Martin
  %A Kloepper, Benjamin
  %A Mawla, Hassan Al
  %A Jäschke, Benjamin
  %A Hollender, Martin
  %A Graube, Markus
  %A Arnu, David
  %A Schmidt, Andreas
  %A Heinze, Sebastian
  %A Schorer, Lukas
  %A Kroll, Andreas
  %A Stumme, Gerd
  %A Urbas, Leon
  %D 2016
  %J atp edition
  %N 9
  %T {Big Data Analytics for Proactive Industrial Decision Support: Approaches \& First Experiences in the Context of the FEE Project}
  %U https://www.atpinfo.de/produkte/2016-big-data-analytics-for-proactive-industrial-decision-support/
  %V 58
  

• 1.

  Atzmueller, M., Schmidt, A., Arnu, D.: {Sequential Modeling and Structural Anomaly Analytics in Industrial Production Environments}. In: Proc. LWA 2016 (KDML Special Track). University of Potsdam, Potsdam, Germany (2016).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ASA:16:LWDA,
  address = {Potsdam, Germany},
  author = {Atzmueller, Martin and Schmidt, Andreas and Arnu, David},
  booktitle = {Proc. LWA 2016 (KDML Special Track)},
  keywords = {itegpub},
  publisher = {University of Potsdam},
  title = {{Sequential Modeling and Structural Anomaly Analytics in Industrial Production Environments}},
  year = 2016
  }
  

  %0 Conference Paper
  %1 ASA:16:LWDA
  %A Atzmueller, Martin
  %A Schmidt, Andreas
  %A Arnu, David
  %B Proc. LWA 2016 (KDML Special Track)
  %C Potsdam, Germany
  %D 2016
  %I University of Potsdam
  %T {Sequential Modeling and Structural Anomaly Analytics in Industrial Production Environments}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2016-atzmueller-anomaly-analytics-lwda.pdf
  

2015

• 1.

  Atzmueller, M.: {Subgroup and Community Analytics}, https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-atzmueller-cssws15-abstract.pdf, (2015).

  URLBibTeXEndNoteBibSonomy

  @misc{Atzmueller:15:CSSWS,
  author = {Atzmueller, Martin},
  editor = {Symposium, Computational Social Science Winter},
  howpublished = {Tutorial Abstract},
  keywords = {community},
  title = {{Subgroup and Community Analytics}},
  year = 2015
  }
  

  %0 Generic
  %1 Atzmueller:15:CSSWS
  %A Atzmueller, Martin
  %D 2015
  %E Symposium, Computational Social Science Winter
  %T {Subgroup and Community Analytics}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-atzmueller-cssws15-abstract.pdf
  

• 1.

  Atzmueller, M., Kibanov, M., Scholz, C., Mueller, J., Stumme, G.: {Conferator – A Ubiquitous System for Enhancing Social Networking at Conferences}. In: Proc. UIS Workshop (2015).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{atzmueller2015conferator,
  author = {Atzmueller, Martin and Kibanov, Mark and Scholz, Christoph and Mueller, Juergen and Stumme, Gerd},
  booktitle = {Proc. UIS Workshop},
  keywords = {myown},
  title = {{Conferator – A Ubiquitous System for Enhancing Social Networking at Conferences}},
  year = 2015
  }
  

  %0 Conference Paper
  %1 atzmueller2015conferator
  %A Atzmueller, Martin
  %A Kibanov, Mark
  %A Scholz, Christoph
  %A Mueller, Juergen
  %A Stumme, Gerd
  %B Proc. UIS Workshop
  %D 2015
  %T {Conferator – A Ubiquitous System for Enhancing Social Networking at Conferences}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-uis-atzmueller-conferator-ubiquitous-social-computing.pdf
  

• 1.

  Kibanov, M.: Mining Groups Stability in Ubiquitous and Social Environments: Communities, Classes and Clusters. In: Cheng, X., Li, H., Gabrilovich, E., and Tang, J. (eds.) Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. pp. 441–446. ACM, Shanghai, China (2015). https://doi.org/10.1145/2684822.2697034.

  URLBibTeXEndNoteDOIBibSonomy

  Ubiquitous Computing is an emerging research area of computer science. Similarly, social network analysis and mining became very important in the last years. We aim to combine these two research areas to explore the nature of processes happening around users. The presented research focuses on exploring and analyzing different groups of persons or entities (communities, clusters and classes), their stability and semantics. An example of ubiquitous social data are social networks captured during scientific conferences using face-to-face RFID proximity tags. Another example of ubiquitous data is crowd-generated environmental sensor data. In this paper we generalize various problems connected to these and further datasets and consider them as a task for measuring group stability. Group stability can be used to improve state-of-the-art methods to analyze data. We also aim to improve the performance of different data mining algorithms, eg. by better handling of data with a skewed density distribution. We describe significant results some experiments that show how the presented approach can be applied and discuss the planned experiments.

  @inproceedings{kibanov2015mining,
  abstract = {Ubiquitous Computing is an emerging research area of computer science. Similarly, social network analysis and mining became very important in the last years. We aim to combine these two research areas to explore the nature of processes happening around users. The presented research focuses on exploring and analyzing different groups of persons or entities (communities, clusters and classes), their stability and semantics. An example of ubiquitous social data are social networks captured during scientific conferences using face-to-face RFID proximity tags. Another example of ubiquitous data is crowd-generated environmental sensor data. In this paper we generalize various problems connected to these and further datasets and consider them as a task for measuring group stability. Group stability can be used to improve state-of-the-art methods to analyze data. We also aim to improve the performance of different data mining algorithms, eg. by better handling of data with a skewed density distribution. We describe significant results some experiments that show how the presented approach can be applied and discuss the planned experiments.},
  address = {New York, NY, USA},
  author = {Kibanov, Mark},
  booktitle = {Proceedings of the Eighth ACM International Conference on Web Search and Data Mining},
  editor = {Cheng, Xueqi and Li, Hang and Gabrilovich, Evgeniy and Tang, Jie},
  keywords = {itegpub},
  pages = {441--446},
  publisher = {ACM},
  series = {WSDM '15},
  title = {Mining Groups Stability in Ubiquitous and Social Environments: Communities, Classes and Clusters},
  year = 2015
  }
  

  %0 Conference Paper
  %1 kibanov2015mining
  %A Kibanov, Mark
  %B Proceedings of the Eighth ACM International Conference on Web Search and Data Mining
  %C New York, NY, USA
  %D 2015
  %E Cheng, Xueqi
  %E Li, Hang
  %E Gabrilovich, Evgeniy
  %E Tang, Jie
  %I ACM
  %P 441--446
  %R 10.1145/2684822.2697034
  %T Mining Groups Stability in Ubiquitous and Social Environments: Communities, Classes and Clusters
  %U http://doi.acm.org/10.1145/2684822.2697034
  %X Ubiquitous Computing is an emerging research area of computer science. Similarly, social network analysis and mining became very important in the last years. We aim to combine these two research areas to explore the nature of processes happening around users. The presented research focuses on exploring and analyzing different groups of persons or entities (communities, clusters and classes), their stability and semantics. An example of ubiquitous social data are social networks captured during scientific conferences using face-to-face RFID proximity tags. Another example of ubiquitous data is crowd-generated environmental sensor data. In this paper we generalize various problems connected to these and further datasets and consider them as a task for measuring group stability. Group stability can be used to improve state-of-the-art methods to analyze data. We also aim to improve the performance of different data mining algorithms, eg. by better handling of data with a skewed density distribution. We describe significant results some experiments that show how the presented approach can be applied and discuss the planned experiments.
  %@ 978-1-4503-3317-7
  

• 1.

  Knoell, D., Rieder, C., Atzmueller, M., Scherer, K.P.: {Towards Generating Test Ontologies using Subgroup Discovery}. In: Proc. LWA 2015 (WM Special Track) (2015).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{KRAS:15,
  author = {Knoell, Daniel and Rieder, Constantin and Atzmueller, Martin and Scherer, Klaus Peter},
  booktitle = {Proc. LWA 2015 (WM Special Track)},
  keywords = {ontology},
  title = {{Towards Generating Test Ontologies using Subgroup Discovery}},
  year = 2015
  }
  

  %0 Conference Paper
  %1 KRAS:15
  %A Knoell, Daniel
  %A Rieder, Constantin
  %A Atzmueller, Martin
  %A Scherer, Klaus Peter
  %B Proc. LWA 2015 (WM Special Track)
  %D 2015
  %T {Towards Generating Test Ontologies using Subgroup Discovery}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-lwa-kdml-sd-ontology-testing.pdf
  

• 1.

  Tran, T., Tran, N.-K., Teka Hadgu, A., Jäschke, R.: Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics (2015).

  BibTeXEndNoteBibSonomy

  In this paper we study the problem of semantic annotation for a trending hashtag which is the crucial step towards analyzing user behavior in social media, yet has been largely unexplored. We tackle the problem via linking to entities from Wikipedia. We incorporate the social aspects of trending hashtags by identifying prominent entities for the annotation so as to maximize the information spreading in entity networks. We exploit temporal dynamics of entities in Wikipedia, namely Wikipedia edits and page views to improve the annotation quality. Our experiments show that we significantly outperform the established methods in tweet annotation.

  @inproceedings{tran2015semantic,
  abstract = {In this paper we study the problem of semantic annotation for a trending hashtag which is the crucial step towards analyzing user behavior in social media, yet has been largely unexplored. We tackle the problem via linking to entities from Wikipedia. We incorporate the social aspects of trending hashtags by identifying prominent entities for the annotation so as to maximize the information spreading in entity networks. We exploit temporal dynamics of entities in Wikipedia, namely Wikipedia edits and page views to improve the annotation quality. Our experiments show that we significantly outperform the established methods in tweet annotation.},
  author = {Tran, Tuan and Tran, Nam-Khanh and Teka Hadgu, Asmelash and Jäschke, Robert},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  keywords = {temporal},
  month = {09},
  publisher = {Association for Computational Linguistics},
  title = {Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information},
  year = 2015
  }
  

  %0 Conference Paper
  %1 tran2015semantic
  %A Tran, Tuan
  %A Tran, Nam-Khanh
  %A Teka Hadgu, Asmelash
  %A Jäschke, Robert
  %B Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  %D 2015
  %I Association for Computational Linguistics
  %T Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information
  %X In this paper we study the problem of semantic annotation for a trending hashtag which is the crucial step towards analyzing user behavior in social media, yet has been largely unexplored. We tackle the problem via linking to entities from Wikipedia. We incorporate the social aspects of trending hashtags by identifying prominent entities for the annotation so as to maximize the information spreading in entity networks. We exploit temporal dynamics of entities in Wikipedia, namely Wikipedia edits and page views to improve the annotation quality. Our experiments show that we significantly outperform the established methods in tweet annotation.
  

• 1.

  Atzmueller, M., Chin, A., Scholz, C., Trattner, C. eds.: {Mining, Modeling and Recommending ’Things’ in Social Media}. Springer Verlag, Heidelberg, Germany (2015).

  BibTeXEndNoteBibSonomy

  @book{ACST:15,
  address = {Heidelberg, Germany},
  booktitle = {MSM/MUSE Postproceedings 2013},
  editor = {Atzmueller, Martin and Chin, Alvin and Scholz, Christoph and Trattner, Christoph},
  journal = {LNCS},
  keywords = {itegpub},
  publisher = {Springer Verlag},
  series = {LNCS},
  title = {{Mining, Modeling and Recommending 'Things' in Social Media}},
  volume = 8940,
  year = 2015
  }
  

  %0 Book
  %1 ACST:15
  %B MSM/MUSE Postproceedings 2013
  %C Heidelberg, Germany
  %D 2015
  %E Atzmueller, Martin
  %E Chin, Alvin
  %E Scholz, Christoph
  %E Trattner, Christoph
  %I Springer Verlag
  %J LNCS
  %T {Mining, Modeling and Recommending 'Things' in Social Media}
  %V 8940
  %@ 978-3-642-23598-6
  

• 1.

  Sîrbu, A., Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V.D.P., Stumme, G., Theunis, J., Tria, F., Van den Bossche, J.: Participatory Patterns in an International Air Quality Monitoring Initiative. PLOS ONE. 10, 1–19 (2015). https://doi.org/10.1371/journal.pone.0136763.

  URLBibTeXEndNoteDOIBibSonomy

  The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.

  @article{sirbu2015participatory,
  abstract = {The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.},
  author = {Sîrbu, Alina and Becker, Martin and Caminiti, Saverio and De Baets, Bernard and Elen, Bart and Francis, Louise and Gravino, Pietro and Hotho, Andreas and Ingarra, Stefano and Loreto, Vittorio and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Servedio, Vito D. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca and Van den Bossche, Joris},
  journal = {PLOS ONE},
  keywords = {itegpub},
  month = {08},
  number = 8,
  pages = {1-19},
  publisher = {Public Library of Science},
  title = {Participatory Patterns in an International Air Quality Monitoring Initiative},
  volume = 10,
  year = 2015
  }
  

  %0 Journal Article
  %1 sirbu2015participatory
  %A Sîrbu, Alina
  %A Becker, Martin
  %A Caminiti, Saverio
  %A De Baets, Bernard
  %A Elen, Bart
  %A Francis, Louise
  %A Gravino, Pietro
  %A Hotho, Andreas
  %A Ingarra, Stefano
  %A Loreto, Vittorio
  %A Molino, Andrea
  %A Mueller, Juergen
  %A Peters, Jan
  %A Ricchiuti, Ferdinando
  %A Saracino, Fabio
  %A Servedio, Vito D. P.
  %A Stumme, Gerd
  %A Theunis, Jan
  %A Tria, Francesca
  %A Van den Bossche, Joris
  %D 2015
  %I Public Library of Science
  %J PLOS ONE
  %N 8
  %P 1-19
  %R 10.1371/journal.pone.0136763
  %T Participatory Patterns in an International Air Quality Monitoring Initiative
  %U https://doi.org/10.1371/journal.pone.0136763
  %V 10
  %X The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.
  

• 1.

  Zoller, D., Doerfel, S., Jäschke, R., Stumme, G., Hotho, A.: On Publication Usage in a Social Bookmarking System. In: Proceedings of the 2015 ACM Conference on Web Science. pp. 67:1–67:2. ACM, Oxford, United Kingdom (2015). https://doi.org/10.1145/2786451.2786927.

  URLBibTeXEndNoteDOIBibSonomy

  Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.

  @inproceedings{zoller2015publication,
  abstract = {Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.},
  address = {New York, NY, USA},
  author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proceedings of the 2015 ACM Conference on Web Science},
  keywords = {bookmarking},
  pages = {67:1--67:2},
  publisher = {ACM},
  series = {WebSci '15},
  title = {On Publication Usage in a Social Bookmarking System},
  year = 2015
  }
  

  %0 Conference Paper
  %1 zoller2015publication
  %A Zoller, Daniel
  %A Doerfel, Stephan
  %A Jäschke, Robert
  %A Stumme, Gerd
  %A Hotho, Andreas
  %B Proceedings of the 2015 ACM Conference on Web Science
  %C New York, NY, USA
  %D 2015
  %I ACM
  %P 67:1--67:2
  %R 10.1145/2786451.2786927
  %T On Publication Usage in a Social Bookmarking System
  %U http://doi.acm.org/10.1145/2786451.2786927
  %X Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.
  %@ 978-1-4503-3672-7
  

• 1.

  Kibanov, M., Atzmueller, M., Illig, J., Scholz, C., Barrat, A., Cattuto, C., Stumme, G.: Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists. In: Proceedings of the 2015 ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, August 25-28, 2015 (2015).

  BibTeXEndNoteBibSonomy

  @inproceedings{kibanov2015content,
  author = {Kibanov, Mark and Atzmueller, Martin and Illig, Jens and Scholz, Christoph and Barrat, Alain and Cattuto, Ciro and Stumme, Gerd},
  booktitle = {Proceedings of the 2015 ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, August 25-28, 2015},
  keywords = {mark},
  title = {Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists},
  year = 2015
  }
  

  %0 Conference Paper
  %1 kibanov2015content
  %A Kibanov, Mark
  %A Atzmueller, Martin
  %A Illig, Jens
  %A Scholz, Christoph
  %A Barrat, Alain
  %A Cattuto, Ciro
  %A Stumme, Gerd
  %B Proceedings of the 2015 ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, August 25-28, 2015
  %D 2015
  %T Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists
  

• 1.

  Singer, P., Helic, D., Hotho, A., Strohmaier, M.: Hyptrails: A bayesian approach for comparing hypotheses about human trails. In: 24th International World Wide Web Conference (WWW2015). ACM, Firenze, Italy (2015).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{singer2015hyptrails,
  address = {Firenze, Italy},
  author = {Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M.},
  booktitle = {24th International World Wide Web Conference (WWW2015)},
  keywords = {bibsonomy},
  month = {05},
  organization = {ACM},
  publisher = {ACM},
  title = {Hyptrails: A bayesian approach for comparing hypotheses about human trails},
  year = 2015
  }
  

  %0 Conference Paper
  %1 singer2015hyptrails
  %A Singer, P.
  %A Helic, D.
  %A Hotho, A.
  %A Strohmaier, M.
  %B 24th International World Wide Web Conference (WWW2015)
  %C Firenze, Italy
  %D 2015
  %I ACM
  %T Hyptrails: A bayesian approach for comparing hypotheses about human trails
  %U http://www.www2015.it/documents/proceedings/proceedings/p1003.pdf
  

• 1.

  Atzmueller, M.: {Subgroup and Community Analytics on Attributed Graphs}. In: Proc. International Workshop on Social Network Analysis using Formal Concept Analysis (SNAFCA-2015). CEUR-WS (2015).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{Atzmueller:15b,
  author = {Atzmueller, Martin},
  booktitle = {Proc. International Workshop on Social Network Analysis using Formal Concept Analysis (SNAFCA-2015)},
  keywords = 2015,
  publisher = {CEUR-WS},
  title = {{Subgroup and Community Analytics on Attributed Graphs}},
  year = 2015
  }
  

  %0 Conference Paper
  %1 Atzmueller:15b
  %A Atzmueller, Martin
  %B Proc. International Workshop on Social Network Analysis using Formal Concept Analysis (SNAFCA-2015)
  %D 2015
  %I CEUR-WS
  %T {Subgroup and Community Analytics on Attributed Graphs}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-atzmueller-snafca-community-analytics.pdf
  

• 1.

  Atzmueller, M., Mueller, J., Becker, M.: {Exploratory Subgroup Analytics on Ubiquitous Data}. In: Mining, Modeling and Recommending ’Things’ in Social Media. Springer Verlag, Heidelberg, Germany (2015).

  BibTeXEndNoteBibSonomy

  @incollection{AMB:15,
  address = {Heidelberg, Germany},
  author = {Atzmueller, Martin and Mueller, Juergen and Becker, Martin},
  booktitle = {Mining, Modeling and Recommending 'Things' in Social Media},
  keywords = {itegpub},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {{Exploratory Subgroup Analytics on Ubiquitous Data}},
  volume = 8940,
  year = 2015
  }
  

  %0 Book Section
  %1 AMB:15
  %A Atzmueller, Martin
  %A Mueller, Juergen
  %A Becker, Martin
  %B Mining, Modeling and Recommending 'Things' in Social Media
  %C Heidelberg, Germany
  %D 2015
  %I Springer Verlag
  %T {Exploratory Subgroup Analytics on Ubiquitous Data}
  %V 8940
  

• 1.

  Atzmueller, M., Doerfel, S., Mitzlaff, F.: {Fast Description-Oriented Community Detection using Subgroup Discovery (Extended Abstract, Resubmission)}. In: Proc. LWA 2015 (KDML Special Track) (2015).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ADM:15b,
  author = {Atzmueller, Martin and Doerfel, Stephan and Mitzlaff, Folke},
  booktitle = {Proc. LWA 2015 (KDML Special Track)},
  keywords = 2015,
  title = {{Fast Description-Oriented Community Detection using Subgroup Discovery (Extended Abstract, Resubmission)}},
  year = 2015
  }
  

  %0 Conference Paper
  %1 ADM:15b
  %A Atzmueller, Martin
  %A Doerfel, Stephan
  %A Mitzlaff, Folke
  %B Proc. LWA 2015 (KDML Special Track)
  %D 2015
  %T {Fast Description-Oriented Community Detection using Subgroup Discovery (Extended Abstract, Resubmission)}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-lwa-kdml-atzmueller.pdf
  

• 1.

  v. Kistowski, J., Nikolas, H., Zoller, D., Kounev, S., Hotho, A.: Modeling and Extracting Load Intensity Profiles. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (2015).

  BibTeXEndNoteBibSonomy

  Today’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.

  @inproceedings{vkistowski2015modeling,
  abstract = {Today’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.},
  author = {v. Kistowski, Jóakim and Nikolas, Herbst. and Zoller, Daniel and Kounev, Samuel and Hotho, Andreas},
  booktitle = {Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)},
  keywords = {extracting},
  title = {Modeling and Extracting Load Intensity Profiles},
  year = 2015
  }
  

  %0 Conference Paper
  %1 vkistowski2015modeling
  %A v. Kistowski, Jóakim
  %A Nikolas, Herbst.
  %A Zoller, Daniel
  %A Kounev, Samuel
  %A Hotho, Andreas
  %B Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)
  %D 2015
  %T Modeling and Extracting Load Intensity Profiles
  %X Today’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.
  

• 1.

  Dallmann, A., Lemmerich, F., Zoller, D., Hotho, A.: Media Bias in German Online Newspapers. In: 26th ACM Conference on Hypertext and Social Media. ACM, Cyprus, Turkey, September 1-4 (2015).

  BibTeXEndNoteBibSonomy

  @inproceedings{dallmann2015media,
  address = {Cyprus, Turkey, September 1-4},
  author = {Dallmann, Alexander and Lemmerich, Florian and Zoller, Daniel and Hotho, Andreas},
  booktitle = {26th ACM Conference on Hypertext and Social Media},
  keywords = {bias},
  publisher = {ACM},
  title = {Media Bias in German Online Newspapers},
  year = 2015
  }
  

  %0 Conference Paper
  %1 dallmann2015media
  %A Dallmann, Alexander
  %A Lemmerich, Florian
  %A Zoller, Daniel
  %A Hotho, Andreas
  %B 26th ACM Conference on Hypertext and Social Media
  %C Cyprus, Turkey, September 1-4
  %D 2015
  %I ACM
  %T Media Bias in German Online Newspapers
  

• 1.

  Schmidt, A., Atzmueller, M., Stumme, G.: The FEE Project: Introduction and First Insights. In: Proc. UIS Workshop (2015).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{schmidt2015project,
  author = {Schmidt, Andreas and Atzmueller, Martin and Stumme, Gerd},
  booktitle = {Proc. UIS Workshop},
  keywords = {myown},
  title = {The FEE Project: Introduction and First Insights},
  year = 2015
  }
  

  %0 Conference Paper
  %1 schmidt2015project
  %A Schmidt, Andreas
  %A Atzmueller, Martin
  %A Stumme, Gerd
  %B Proc. UIS Workshop
  %D 2015
  %T The FEE Project: Introduction and First Insights
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-uis-big-data-fee-first-insights.pdf
  

• 1.

  Pujari, S.C., Teka Hadgu, A., Lex, E., Jäschke, R.: Social Activity versus Academic Activity: A Case Study of Computer Scientists on Twitter. In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business. ACM, New York, NY, USA (2015). https://doi.org/10.1145/2809563.2809584.

  URLBibTeXEndNoteDOIBibSonomy

  In this work, we study social and academic network activities of researchers from Computer Science. Using a recently proposed framework, we map the researchers to their Twitter accounts and link them to their publications. This enables us to create two types of networks: first, networks that reflect social activities on Twitter, namely the researchers’ follow, retweet and mention networks and second, networks that reflect academic activities, that is the co-authorship and citation networks. Based on these datasets, we (i) compare the social activities of researchers with their academic activities, (ii) investigate the consistency and similarity of communities within the social and academic activity networks, and (iii) investigate the information flow between different areas of Computer Science in and between both types of networks. Our findings show that if co-authors interact on Twitter, their relationship is reciprocal, increasing with the numbers of papers they co-authored. In general, the social and the academic activities are not correlated. In terms of community analysis, we found that the three social activity networks are most consistent with each other, with the highest consistency between the retweet and mention network. A study of information flow revealed that in the follow network, researchers from Data Management, Human-Computer Interaction, and Artificial Intelligence act as a source of information for other areas in Computer Science.

  @inproceedings{pujari2015social,
  abstract = {In this work, we study social and academic network activities of researchers from Computer Science. Using a recently proposed framework, we map the researchers to their Twitter accounts and link them to their publications. This enables us to create two types of networks: first, networks that reflect social activities on Twitter, namely the researchers’ follow, retweet and mention networks and second, networks that reflect academic activities, that is the co-authorship and citation networks. Based on these datasets, we (i) compare the social activities of researchers with their academic activities, (ii) investigate the consistency and similarity of communities within the social and academic activity networks, and (iii) investigate the information flow between different areas of Computer Science in and between both types of networks. Our findings show that if co-authors interact on Twitter, their relationship is reciprocal, increasing with the numbers of papers they co-authored. In general, the social and the academic activities are not correlated. In terms of community analysis, we found that the three social activity networks are most consistent with each other, with the highest consistency between the retweet and mention network. A study of information flow revealed that in the follow network, researchers from Data Management, Human-Computer Interaction, and Artificial Intelligence act as a source of information for other areas in Computer Science.},
  address = {New York, NY, USA},
  author = {Pujari, Subhash Chandra and Teka Hadgu, Asmelash and Lex, Elisabeth and Jäschke, Robert},
  booktitle = {Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business},
  keywords = {network},
  publisher = {ACM},
  series = {i-KNOW '15},
  title = {Social Activity versus Academic Activity: A Case Study of Computer Scientists on Twitter},
  year = 2015
  }
  

  %0 Conference Paper
  %1 pujari2015social
  %A Pujari, Subhash Chandra
  %A Teka Hadgu, Asmelash
  %A Lex, Elisabeth
  %A Jäschke, Robert
  %B Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business
  %C New York, NY, USA
  %D 2015
  %I ACM
  %R 10.1145/2809563.2809584
  %T Social Activity versus Academic Activity: A Case Study of Computer Scientists on Twitter
  %U http://dx.doi.org/10.1145/2809563.2809584
  %X In this work, we study social and academic network activities of researchers from Computer Science. Using a recently proposed framework, we map the researchers to their Twitter accounts and link them to their publications. This enables us to create two types of networks: first, networks that reflect social activities on Twitter, namely the researchers’ follow, retweet and mention networks and second, networks that reflect academic activities, that is the co-authorship and citation networks. Based on these datasets, we (i) compare the social activities of researchers with their academic activities, (ii) investigate the consistency and similarity of communities within the social and academic activity networks, and (iii) investigate the information flow between different areas of Computer Science in and between both types of networks. Our findings show that if co-authors interact on Twitter, their relationship is reciprocal, increasing with the numbers of papers they co-authored. In general, the social and the academic activities are not correlated. In terms of community analysis, we found that the three social activity networks are most consistent with each other, with the highest consistency between the retweet and mention network. A study of information flow revealed that in the follow network, researchers from Data Management, Human-Computer Interaction, and Artificial Intelligence act as a source of information for other areas in Computer Science.
  

• 1.

  Ring, M., Otto, F., Becker, M., Niebler, T., Landes, D., Hotho, A.: ConDist: A Context-Driven Categorical Distance Measure. Presented at the (2015).

  BibTeXEndNoteBibSonomy

  @inproceedings{ring2015condist,
  author = {Ring, Markus and Otto, Florian and Becker, Martin and Niebler, Thomas and Landes, Dieter and Hotho, Andreas},
  editor = {ECMLPKDD2015},
  keywords = {categorical},
  title = {ConDist: A Context-Driven Categorical Distance Measure},
  year = 2015
  }
  

  %0 Conference Paper
  %1 ring2015condist
  %A Ring, Markus
  %A Otto, Florian
  %A Becker, Martin
  %A Niebler, Thomas
  %A Landes, Dieter
  %A Hotho, Andreas
  %D 2015
  %E ECMLPKDD2015,
  %T ConDist: A Context-Driven Categorical Distance Measure
  

• 1.

  Kibanov, M., Amin, I., Lee, J.G.: Supporting Peat Fire Management using Social Media, (2015).

  BibTeXEndNoteBibSonomy

  Peat fires and haze originating from such fires cause a large spectrum of ecological, social and economical problems as well as health issues. Indonesia is a country where peat fires are prevalent and the population suffers from the ensuing haze problems. Two Indonesian islands (Sumatra and Kalimantan) are most affected by peat fires. In this abstract, considering the fact that the usage of social media in Indonesia is high (with 72 million accounts in social networks), it is expected that insights generated from social media can help central and local authorities to improve peat fire management. In particular, we focus on peat fires in the year 2014 in Riau Province, which is one of the most haze-affected areas on Sumatra Island.

  @misc{kibanov2015supporting,
  abstract = {Peat fires and haze originating from such fires cause a large spectrum of ecological, social and economical problems as well as health issues. Indonesia is a country where peat fires are prevalent and the population suffers from the ensuing haze problems. Two Indonesian islands (Sumatra and Kalimantan) are most affected by peat fires. In this abstract, considering the fact that the usage of social media in Indonesia is high (with 72 million accounts in social networks), it is expected that insights generated from social media can help central and local authorities to improve peat fire management. In particular, we focus on peat fires in the year 2014 in Riau Province, which is one of the most haze-affected areas on Sumatra Island.},
  author = {Kibanov, Mark and Amin, Imaduddin and Lee, Jong Gun},
  howpublished = {Computational Social Science Winter Symposium 2015, Poster},
  keywords = {itegpub},
  title = {Supporting Peat Fire Management using Social Media},
  year = 2015
  }
  

  %0 Generic
  %1 kibanov2015supporting
  %A Kibanov, Mark
  %A Amin, Imaduddin
  %A Lee, Jong Gun
  %D 2015
  %T Supporting Peat Fire Management using Social Media
  %X Peat fires and haze originating from such fires cause a large spectrum of ecological, social and economical problems as well as health issues. Indonesia is a country where peat fires are prevalent and the population suffers from the ensuing haze problems. Two Indonesian islands (Sumatra and Kalimantan) are most affected by peat fires. In this abstract, considering the fact that the usage of social media in Indonesia is high (with 72 million accounts in social networks), it is expected that insights generated from social media can help central and local authorities to improve peat fire management. In particular, we focus on peat fires in the year 2014 in Riau Province, which is one of the most haze-affected areas on Sumatra Island.
  

• 1.

  Atzmueller, M.: {Subgroup Discovery - Advanced Review}. WIREs: Data Mining and Knowledge Discovery. 5, 35–49 (2015). https://doi.org/10.1002/widm.1144.

  URLBibTeXEndNoteDOIBibSonomy

  Subgroup discovery is a broadly applicable descriptive data mining technique for identifying interesting subgroups according to some property of interest. This article summarizes fundamentals of subgroup discovery, before it reviews algorithms and further advanced methodological issues. In addition, we briefly discuss tools and applications of subgroup discovery approaches. In that context, we also discuss experiences and lessons learned and outline future directions in order to show the advantages and benefits of subgroup discovery.

  @article{Atzmueller:15a,
  abstract = {Subgroup discovery is a broadly applicable descriptive data mining technique for identifying interesting subgroups according to some property of interest. This article summarizes fundamentals of subgroup discovery, before it reviews algorithms and further advanced methodological issues. In addition, we briefly discuss tools and applications of subgroup discovery approaches. In that context, we also discuss experiences and lessons learned and outline future directions in order to show the advantages and benefits of subgroup discovery.},
  author = {Atzmueller, Martin},
  journal = {WIREs: Data Mining and Knowledge Discovery},
  keywords = {itegpub},
  number = 1,
  pages = {35--49},
  title = {{Subgroup Discovery - Advanced Review}},
  volume = 5,
  year = 2015
  }
  

  %0 Journal Article
  %1 Atzmueller:15a
  %A Atzmueller, Martin
  %D 2015
  %J WIREs: Data Mining and Knowledge Discovery
  %N 1
  %P 35--49
  %R 10.1002/widm.1144
  %T {Subgroup Discovery - Advanced Review}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-subgroup-discovery-advanced-review-wires-2015.pdf
  %V 5
  %X Subgroup discovery is a broadly applicable descriptive data mining technique for identifying interesting subgroups according to some property of interest. This article summarizes fundamentals of subgroup discovery, before it reviews algorithms and further advanced methodological issues. In addition, we briefly discuss tools and applications of subgroup discovery approaches. In that context, we also discuss experiences and lessons learned and outline future directions in order to show the advantages and benefits of subgroup discovery.
  

• 1.

  Doerfel, S., Jäschke, R., Stumme, G.: The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems. Transactions on Intelligent Systems and Technology. (2015).

  BibTeXEndNoteBibSonomy

  @article{doerfel2015cores,
  author = {Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd},
  journal = {Transactions on Intelligent Systems and Technology},
  keywords = {benchmark},
  publisher = {ACM},
  title = {The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems},
  year = 2015
  }
  

  %0 Journal Article
  %1 doerfel2015cores
  %A Doerfel, Stephan
  %A Jäschke, Robert
  %A Stumme, Gerd
  %D 2015
  %I ACM
  %J Transactions on Intelligent Systems and Technology
  %T The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems
  

• 1.

  Atzmueller, M., Lemmerich, F. eds.: {Proceedings of the 2015 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2015)}. CEUR-WS, Porto, Portugal (2015).

  URLBibTeXEndNoteBibSonomy

  @book{AL:15,
  address = {Porto, Portugal},
  editor = {Atzmueller, Martin and Lemmerich, Florian},
  keywords = {itegpub},
  publisher = {CEUR-WS},
  title = {{Proceedings of the 2015 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2015)}},
  volume = 1521,
  year = 2015
  }
  

  %0 Book
  %1 AL:15
  %C Porto, Portugal
  %D 2015
  %E Atzmueller, Martin
  %E Lemmerich, Florian
  %I CEUR-WS
  %T {Proceedings of the 2015 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2015)}
  %U http://ceur-ws.org/Vol-1521/
  %V 1521
  

• 1.

  Kibanov, M., Atzmueller, M., Illig, J., Scholz, C., Barrat, A., Cattuto, C., Stumme, G.: {Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists (Poster)}, http://www.gesis.org/css-wintersymposium/program/poster-sessions-presentations/, (2015).

  URLBibTeXEndNoteBibSonomy

  @misc{KAISBCS:15p,
  author = {Kibanov, Mark and Atzmueller, Martin and Illig, Jens and Scholz, Christoph and Barrat, Alain and Cattuto, Ciro and Stumme, Gerd},
  howpublished = {Computational Social Science Winter Symposium 2015, Poster},
  keywords = {face-to-face},
  title = {{Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists (Poster)}},
  year = 2015
  }
  

  %0 Generic
  %1 KAISBCS:15p
  %A Kibanov, Mark
  %A Atzmueller, Martin
  %A Illig, Jens
  %A Scholz, Christoph
  %A Barrat, Alain
  %A Cattuto, Ciro
  %A Stumme, Gerd
  %D 2015
  %T {Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists (Poster)}
  %U http://www.gesis.org/css-wintersymposium/program/poster-sessions-presentations/
  

• 1.

  Atzmueller, M.: {Subgroup Discovery and Community Detection on Attributed Graphs}, https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-atzmueller-asonam15-abstract.pdf, (2015).

  URLBibTeXEndNoteBibSonomy

  @misc{Atzmueller:15:ASONAM,
  author = {Atzmueller, Martin},
  editor = {ASONAM},
  howpublished = {ASONAM 2015, Tutorial Abstract},
  keywords = {community},
  title = {{Subgroup Discovery and Community Detection on Attributed Graphs}},
  year = 2015
  }
  

  %0 Generic
  %1 Atzmueller:15:ASONAM
  %A Atzmueller, Martin
  %D 2015
  %E ASONAM,
  %T {Subgroup Discovery and Community Detection on Attributed Graphs}
  %U https://www.kde.cs.uni-kassel.de/atzmueller/paper/2015-atzmueller-asonam15-abstract.pdf
  

• 1.

  Atzmueller, M., Kibanov, M., Hayat, N., Trojahn, M., Kroll, D.: {Adaptive Class Association Rule Mining for Human Activity Recognition}. In: Proceedings of the International Workshop on Mining Ubiquitous and Social Environments (MUSE2015). , Porto, Portugal (2015).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AKHTK:15,
  address = {Porto, Portugal},
  author = {Atzmueller, Martin and Kibanov, Mark and Hayat, Naveed and Trojahn, Matthias and Kroll, Dennis},
  booktitle = {Proceedings of the International Workshop on Mining Ubiquitous and Social Environments (MUSE2015)},
  keywords = {itegpub},
  title = {{Adaptive Class Association Rule Mining for Human Activity Recognition}},
  year = 2015
  }
  

  %0 Conference Paper
  %1 AKHTK:15
  %A Atzmueller, Martin
  %A Kibanov, Mark
  %A Hayat, Naveed
  %A Trojahn, Matthias
  %A Kroll, Dennis
  %B Proceedings of the International Workshop on Mining Ubiquitous and Social Environments (MUSE2015)
  %C Porto, Portugal
  %D 2015
  %T {Adaptive Class Association Rule Mining for Human Activity Recognition}
  %U https://www.kde.cs.uni-kassel.de/ws/muse2015/papers/atzmueller.pdf