News

2010-04-01: The web page for the workshop is now online.

2010-08-01: The list of accepted papers is now online.

2010-09-15: The MUSE 2010 workshop proceedings are now available.

Important dates

*NEW Submission deadlines*

  • Abstract Submission: Wednesday, June 23rd, 2010
  • Paper Submission: Monday, June 28th, 2010
  • Acceptance Notification: Wednesday, July 21st, 2010
  • Paper Final Version Due: Friday, July 30th, 2010
  • Early Registration Deadline: Saturday, July 31st, 2010
  • Workshop: Monday, Sep 20th, 2010

Objectives

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 semantic web also integrates the user interactions in social networking environments. Mining in ubiquitous and social environments is thus an emerging area of research focusing on advanced systems for data mining in such distributed and network-organized systems. It also integrates some related technologies such as activity recognition, Web 2.0 mining, privacy issues and privacy-preserving mining, predicting user behavior, etc.

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 are in general 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. As there is only minimal coordination, these sources can overlap or diverge in any possible way. 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.

The goal of this workshop is to promote an interdisciplinary forum for researchers working in the fields of ubiquitous computing, social semantic web, Web 2.0, and social networks which are interested in utilizing data mining in an ubiquitous setting. The workshop seeks for contributions applying state-of-the-art mining algorithms on ubiquitous and social data. Papers focusing on the intersection 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 three areas which include, but are not limited to the following topics:

  • Sensors and mobile devices:
    • Mining ubiquitous user models
    • 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
    • Sensor data preprocessing, transformation, and space-time sampling techniques
  • User behavior:
    • Personalization and recommendation
    • Predicting user behavior
    • User profiling in ubiquitous and social environments
    • Mining continuous streams and ubiquitous data
    • Network analysis of social systems
    • Discovering social structures and communities
    • Combining data from different sources, mining with mashups
  • Applications:
    • Discovering misuse and fraud
    • Usage and presentation interfaces for mining and data collection
    • Privacy challenges in ubiquitous and social applications
    • Applications of any of the above methods and technologies

We also encourage submissions which relate research results from other areas to the workshop topics.

It is planned to publish revised selected papers (jointly with papers from the Workshop on Modeling Social Media) as a volume in the Springer LNCS/LNAI series.

Workshop Organizers

  • Martin Atzmueller, Knowledge and Data Engineering Group, University of Kassel, Germany
    ( )
  • Andreas Hotho, Data Mining and Information Retrieval Group, University of Wuerzburg, Germany
    ( )

Proceedings

Download the MUSE proceedings here.

Program

Program Committee

  • Harith Alani, KMi, Open University, United Kingdom
  • Bettina Berendt, Katholieke Universiteit Leuven, Belgium
  • Ciro Cattuto, ISI Foundation, Italy
  • Michelangelo Ceci, University of Bari, Italy
  • Marco Degemmis, University of Bari, Italy
  • Joao Gama, University of Porto, Portugal
  • Michael May, Fraunhofer IAIS, Germany
  • Mark Meiss, University of Indiana, USA
  • Dunja Mladenic, J.Stefan Institute, Slovenia
  • Katharina Morik, University of Dortmund, Germany
  • Claudia Müller-Birn, Carnegie Mellon University, USA
  • Ion Muslea, SRI International, Menlo Park, USA
  • Harald Sack, Hasso-Plattner-Institut, University of Potsdam, Germany
  • Giovanni Semeraro, University of Bari, Italy
  • Sergej Sizov, University of Koblenz-Landau, Germany
  • Avare Stewart, L3S Research Center, Germany
  • Gerd Stumme, University of Kassel, Germany
  • Maarten van Someren, University of Amsterdam, The Netherlands
  • Koen Vanhoof, University of Hasselt, Belgium
  • Denny Vrandecic, KIT, Germany
  • Michael Wurst, IBM Research, Germany

Accepted papers

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:

We recommend to follow the format guidelines of ECML/PKDD (Springer LNCS), as this will be the required format for accepted papers (cf. instructions).