Frequently and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Developmental Peer Network Relationships.Thiele, Lisa; Atzmueller, Martin; Stumme, Gerd; Kauffeld, Simone (2018).
The Promise of Data Science Methods for Research on Entrepreneurial Decision Making: Detecting Social Signals during Human Interactions.Liebregts, Werner; Darnihamedani, Pourya; Postma, Eric; Atzmueller, Martin (2018).
Challenges for Big and Smart Data in Process Industries.Folmer, Jens; Kirchen, Iris; Trunzer, Emanuel; Vogel-Heuser, Birgit; Pötter, Thorsten; Graube, Markus; Heinze, Sebastian; Urbas, Leon; Atzmueller, Martin; Arnu, David (2017). 01-02
Applications for Environmental Sensing in
EveryAware.Atzmueller, Martin; Becker, Martin; Molino, Andrea; Mueller, Juergen; Peters, Jan; Sirbu, Alina V. Loreto, Haklay, M., Hotho, A., Servedio, V. D. P., Stumme, G., Tria, F., Theunis, J. (eds.) (2017).
Observing Human Activity Through Sensing.Gautama, Sidharta; Atzmueller, Martin; Kostakos, Vasillis; Gillis, Dominique; Hosio, Simo V. Loreto, Haklay, M., Hotho, A., Servedio, V. D. P., Stumme, G., Tria, F., Theunis, J. (eds.) (2017).
The Co-Evolution of Career Aspirations and Peer Relationships in Psychology Bachelor Students: A Longitudinal Social Network Study.Thiele, Lisa; Sauer, Nils Christian; Atzmueller, Martin; Kauffeld, Simone (2017).
Explanation-Aware Feature Selection using Symbolic Time Series Abstraction: Approaches and Experiences in a Petro-Chemical Production Context.Atzmueller, Martin; Hayat, Naveed; Schmidt, Andreas; Klöpper, Benjamin (2017).
Big Data Analytics in the Social and Ubiquitous ContextAtzmueller, Martin; Chin, Alvin; Janssen, Frederik; Schweizer, Immanuel; Trattner, Christoph in Lecture Notes in Computer Science (2016). (Vol. 9546) Springer Verlag, Heidelberg, Germany.
Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists.Kibanov, Mark; Atzmueller, Martin; Illig, Jens; Scholz, Christoph; Barrat, Alain; Cattuto, Ciro; Stumme, Gerd (2015).
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.
Exploratory Subgroup Analytics on Ubiquitous Data.Atzmueller, Martin; Mueller, Juergen; Becker, Martin (2015). (Vol. 8940)
Is Web Content a Good Proxy for Real-Life Interaction? A Case Study Considering Online and Offline Interactions of Computer Scientists (Poster).Kibanov, Mark; Atzmueller, Martin; Illig, Jens; Scholz, Christoph; Barrat, Alain; Cattuto, Ciro; Stumme, Gerd (2015).
Mining Social Links for Ubiquitous Knowledge Engineering.Scholz, Christoph; Macek, Bjoern-Elmar; Atzmueller, Martin; Doerfel, Stephan; Stumme, Gerd K. David, Geihs, K., Leimeister, J. -M., Roßnagel, A., Schmidt, L., Stumme, G., Wacker, A. (eds.) (2014).
Connect-U: A System for Enhancing Social Networking.Atzmueller, Martin; Behrenbruch, Kay; Hoffmann, Axel; Kibanov, Mark; Macek, Bjoern-Elmar; Scholz, Christoph; Skistims, Hendrik; Söllner, Matthias; Stumme, Gerd K. David, Geihs, K., Leimeister, J. -M., Roßnagel, A., Schmidt, L., Stumme, G., Wacker, A. (eds.) (2014).
Subjective versus Objective Captured Social Networks: Comparing Standard Self-Report Questionnaire Data with Observational RFID Technology Data.Thiele, Lisa; Atzmueller, Martin; Kauffeld, Simone; Stumme, Gerd (2014).
Sensor data is objective. But when measuring our environment, measured values are contrasted with our perception, which is always subjective. This makes interpreting sensor measurements difficult for a single person in her personal environment. In this context, the EveryAware projects directly connects the concepts of objective sensor data with subjective impressions and perceptions by providing a collective sensing platform with several client applications allowing to explicitly associate those two data types. The goal is to provide the user with personalized feedback, a characterization of the global as well as her personal environment, and enable her to position her perceptions in this global context.
In this poster we summarize the collected data of two EveryAware applications, namely WideNoise for noise measurements and AirProbe for participatory air quality sensing. Basic insights are presented including user activity, learning processes and sensor data to perception correlations. These results provide an outlook on how this data can further be used to understand the connection between sensor data and perceptions.
Proceedings of the 2014 International Conference on Social Computing,
Beijing, China, August 04 - 07, 2014Yang, Su; Lerman, Kristina; She, James; Atzmueller, Martin (2014). ACM.