2015-03-29: The web page for the workshop is now online.
- Paper Submission:
June 22ndJune 28th (extended), 2015
- Acceptance Notification:
Monday, July 13th, 2015
- Paper Final Version Due:
Monday, July 27th, 2015
Monday, Sep 7th, 2015
The emergence of ubiquitous computing has started to create new environments consisting of small, heterogeneous, and distributed devices that foster the social interaction of users in several dimensions. Similarly, the upcoming social web also integrates the user interactions in social networking environments.
In typical ubiquitous settings, the mining system can be implemented inside the small devices and sometimes on central servers, for real-time applications, similar to common mining approaches. However, the characteristics of ubiquitous and social mining in general are quite different from the current mainstream data mining and machine learning. Unlike in traditional data mining scenarios, data does not emerge from a small number of (heterogeneous) data sources, but potentially from hundreds to millions of different sources. Often there is only minimal coordination and thus these sources can overlap or diverge in many possible ways. Steps into this new and exciting application area are the analysis of this new data, the adaptation of well known data mining and machine learning algorithms and finally the development of new algorithms.
Mining big data in ubiquitous and social environments is an emerging area of research focusing on advanced systems for data mining in such distributed and network-organized systems. Therefore, for this workshop, we aim to attract researchers from all over the world working in the field of data mining and machine learning with a special focus on analyzing big data in ubiquitous and social environments.
The goal of this workshop is to promote an interdisciplinary forum for researchers working in the fields of ubiquitous computing, mobile sensing, social web, Web 2.0, and social networks which are interested in utilizing data mining in a ubiquitous setting. The workshop seeks for contributions adopting state-of-the-art mining algorithms on ubiquitous social data. Papers combining aspects of the two fields are especially welcome. In short, we want to accelerate the process of identifying the power of advanced data mining operating on data collected in ubiquitous and social environments, as well as the process of advancing data mining through lessons learned in analyzing these new data.
Topics of Interest
The topics of the workshop are split roughly into four areas which include, but are not limited to the following topics:
- Ubiquitous Mining:
- Analysis of data from sensors and mobile devices
- Resource-aware algorithms for distributed mining
- Scalable and distributed classification, prediction, and clustering algorithms
- Activity recognition
- Mining continuous streams and ubiquitous data
- Online methods for mining temporal, spatial and spatio-temporal data
- Combining data from different sources
- Mining Social Data:
- Analysis of social networks and social media
- Mining techniques for social networks and social media
- Algorithms for inferring semantics and meaning from social data
- Privacy and security issues in social data
- How social data can be used to mine and create collective intelligence
- Individual and group behavior in social media and social networks
- Social networks for the collaboration of large communities
- Modeling social behavior
- Novel techniques for mining big data from social media
- Dynamics and evolution patterns of social networks
- Ubiquitous and Social Mining
- Personalization and recommendation
- User models and predicting user behavior
- User profiling in ubiquitous and social environments
- Network analysis of social systems
- Discovering social structures and communities
- Mobility mining
- Link prediction
- Analysis of data from crowd-sourcing approaches
- Discovering misuse and fraud
- Usage and presentation interfaces for mining and data collection
- Analysis of social and ubiquitous games
- Privacy challenges in ubiquitous and social applications
- Recommenders in ubiquitous and social environments
- Applications of any of the above methods and technologies
We also encourage submissions which relate research results from other areas to the workshop topics.
Springer Book: As in the previous years, it is planned to publish revised selected papers as a volume in the Springer LNCS/LNAI series.
- Martin Atzmueller, Knowledge and Data Engineering Group, Kassel University, Germany
- Florian Lemmerich, GESIS, Koeln, Germany
- Christian Bauckhage, Fraunhofer IAIS, Germany
- Martin Becker, University of Wuerzburg, Germany
- Albert Bifet, University of Waikato, New Zealand
- Stephan Doerfel, University of Kassel, Germany
- Jill Freyne, CSIRO, Australia
- Andreas Hotho, University of Wuerzburg, Germany
- Mark Kibanov, University of Kassel, Germany
- Claudia Mueller-Birn, FU Berlin, Germany
- Nico Piatkowski, TU Dortmund University, Germany
- Haggai Roitman, IBM Research Haifa, Israel
- Philipp Singer, GESIS Koeln, Germany
- Maarten van Someren, University of Amsterdam, The Netherlands
- Gerd Stumme, University of Kassel, Germany
- Arkaitz Zubiaga, University of Warwick, UK
- 9:00 - 10:30 Session 1: Multidimensional Analysis of Social Media
- 9:00 - 9:05 Welcome and Introduction
- 9:05 - 10:00 Michele Berlingerio: Multidimensional Network Analysis: Models, Analytics, Applications. (Invited Talk) PDF
- 10:00 - 10:30 Kezban Dilek Kisa and Pinar Karagoz. Named Entity Recognition from Scratch on Social Media PDF
- 11:00 - 12:45 Session 2: Ubiquitous Social Media Analytics
- 11:00 - 11:55 Markus Schedl: Listener-aware Music Search and Recommendation (Invited Talk) PDF
- 11:55 - 12:25 Martin Atzmueller, Mark Kibanov, Naveed Hayat, Matthias Trojahn and Dennis Kroll. Adaptive Class Association Rule Mining for Human Activity Recognition PDF
- 12:25 - 12:45 Matt Revelle, Carlotta Domeniconi and Aditya Johri. Evidence of Temporal Artifacts in Social Networks PDF
- 14:00 - 15:30 Session 3: Scalable Big Data Analytics
- 14:00 - 14:30 Avijit Saha, Rishabh Misra and Balaraman Ravindran. Scalable Bayesian Matrix Factorization PDF
- 14:30 - 15:25 Albert Bifet: Mining Big Data Streams with Apache SAMOA (Invited Talk) PDF
- 14:25 - 15:30 Closing
Submission and Proceedings
We invite two types of submissions for this workshop:
- Technical papers in any of the topics of interest of the workshop (but not limited to them)
- Short position papers in any of the topics of interest of the workshop (but not limited to them)
Submitted papers will be peer-reviewed and selected on the basis of these reviews. Accepted papers will be presented at the workshop.
Format requirements for submissions of papers are:
- Maximum 16 pages, including title page and bibliography for technical papers.
- Maximum 8 pages, including title page and bibliography for short position papers.
- All submissions must be
entered into the reviewing system.
If you have any question please contact the .
We recommend to follow the format guidelines of ECML/PKDD (Springer LNCS), as this will be the required format for accepted papers (cf. instructions).