News

2013-04-02: The web page for the workshop is now online.

Important dates

Submission deadlines

  • Abstract Submission:
    Friday, June 21st, 2013
  • Paper Submission:
    Friday, June 28th, 2013
  • Acceptance Notification:
    Friday, July 19th, 2013
  • Paper Final Version Due:
    Friday, August 2nd, 2013
  • Workshop:
    Monday, Sep 23rd, 2013

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 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, social web 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. 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.

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
    • Sensor data preprocessing, transformation, and space-time sampling techniques
  • 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
    • Social networks for the collaboration of large communities
    • Modeling social behavior
    • Dynamics and evolution patterns of social networks
    • Link prediction
  • 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
    • Analysis of data from crowd-sourcing approaches
    • Group formation and evolution
    • Mobility Mining
  • Applications:
    • 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.

Workshop Organizers

  • Martin Atzmueller, Knowledge and Data Engineering Group, Kassel University, Germany
    ( )
  • Christoph Scholz, Knowledge and Data Engineering Group, Kassel University, Germany
    ( )

Program Committee

  • Albert Bifet, University of Waikato, New Zealand.
  • Ciro Cattuto, ISI Foundation, Italy
  • Michelangelo Ceci, University of Bari, Italy
  • Jill Freyne, CSIRO, Australia
  • Daniel Gayo Avello, University of Oviedo, Spain
  • Ido Guy, IBM Research
  • Andreas Hotho, University of Wuerzburg, Germany
  • Kristian Kersting, Fraunhofer IAIS and University of Bonn, Germany
  • Florian Lemmerich, University of Wuerzburg, Germany
  • Claudia Mueller-Birn, FU Berlin, Germany
  • Haggai Roitman, IBM Research Haifa, Israel
  • Giovanni Semeraro, University of Bari, Italy
  • Philipp Singer, Graz University of Technology, Austria
  • Maarten van Someren, University of Amsterdam, The Netherlands
  • Gerd Stumme, University of Kassel, Germany
  • Arkaitz Zubiaga, City University of New York, USA

Proceedings

Download the MUSE proceedings here.

Program

  • 9:00-10:30: Session 1
    • 9:00 - 9:15: Welcome and Introduction
    • 9:15 - 10:30: Invited Talk - Francesco Bonchi: Mining Information Propagation Traces in Social Networks
      With the success of online social networks and microblogging platforms such as Facebook, Flickr and Twitter, the phenomenon of influence-driven propagations, has recently attracted the interest of computer scientists, information technologists, and marketing specialists.
      In this talk we take a data mining perspective and we discuss what (and how) can be learned from a social network and a database of traces of past propagations over the social network. Starting from one of the key problems in this area, i.e. the identification of influential users, by targeting whom certain desirable marketing outcomes can be achieved, we provide an overview of some recent progresses in this area and discuss some open problems.
  • 10:30 - 11:00: Coffee break

  • 11:00 - 12:20: Session 2
    • 11:00 - 11:40:Danaipat Sodkomkham, Roberto Legaspi, Ken-ichi Fukui, Koichi Moriyama, Satoshi Kurihara, and Masayuki Numao: Aperiodic and Periodic Model for Long-Term Human Mobility Prediction Using Ambient Simple Sensors
    • 11:40 - 12:00: Martin Atzmueller and Juergen Mueller: Subgroup Analytics and Interactive Assessment on Ubiquitous Data
    • 12:00 - 12:20: Jochen Streicher, Nico Piatkowski, Katharina Morik and Olaf Spinczyk: Open Smartphone Data for Mobility and Utilization Analysis in Ubiquitous Environments
  • 12:20 - 14:00: Lunch

  • 14:00 - 15:30: Session 3
    • 14:00 - 14:20: Saurabh Khanwalkar, Marc Seldin, Amit Srivastava, Anoop Kumar and Sean Colbath: Content-Based Geo-Location Detection for Placing Tweets Pertaining To Trending News on Map
    • 14:20 - 15:00: Emmanouil Tzouridis and Ulf Brefeld: Learning the Shortest Path for Text Summarisation
    • 15:00 - 15:30: Discussion + 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:

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