Joint 12th European Conference on Machine Learning (ECML'01)
and 5th European Conference on Principles and Practice of Knowledge
Discovery in Databases (PKDD'01)
September 3-7, 2001, Freiburg, Germany
Hillol Kargupta, University of Maryland Baltimore County,
(410) 455-3972, firstname.lastname@example.org.
Krishnamoorthy Sivakumar, Washington State University, USA
(509) 335-4969, email@example.com.
Ruediger Wirth, DaimlerChrysler AG, Germany
+49 731 505 2946, firstname.lastname@example.org.
Elisa Bertino, University of Milan, Italy
Bertrand du Castel, Schlumberger, USA
Pete Edwards, University of Aberdeen, UK
Joydeep Ghosh, The University of Texas at Austin, USA
Robert Grossman, University of Illinois at Chicago, USA
Jochen Hipp, Wilhelm-Schickard-Institute, Germany
Anupam Joshi, University of Maryland Baltimore County, USA
Vipin Kumar, University of Minnesota, USA
Michael May, GMD - German National Research Center for Information
Sally McClean, University of Ulster, UK
Erich Neuhold, GMD - German National Research Center for Information
Technology and University of Darmstadt, Germany
Andreas L. Prodromidis, Columbia University, USA
Mehmet Sayal, HP Lab, USA
Nandit Soparkar, University of Michigan, USA
Parthasarathy Srinivasan, Ohio State University, USA
Peter I. Scheuermann, Northwestern University, USA
Yelena Yesha, University of Maryland Baltimore County, USA
Scope of the Workshop:
Knowledge discovery and data mining (KDD) deal with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. KDD is playing an increasingly important role business, scientific, and engineering applications because of the growing availability of data in electronic format. The advent of laptops, palmtops, cell phones, and wearable computers is also making ubiquitous access to large quantity of data possible. Advanced analysis of data for extracting useful knowledge is the next natural step in the world of ubiquitous computing.
This workshop will focus on the state-of-the-art technology for ubiquitous
data mining (UDM) in mobile and distributed environments. Accessing and
analyzing data from a ubiquitous computing device offer many challenges.
For example, the benefits of ubiquitous presence usually do not come for
free. UDM introduces additional cost due to communication, computation,
security, and other factors. So one of the objectives of UDM is to mine
data while minimizing the cost of ubiquitous presence.Human-computer interaction
is another challenging aspect of UDM. Visualizing patterns like, classifiers,
clusters, associations and others, in portable devices are usually difficult.
The small display areas offer serious challenges to interactive data mining
environments. Data management in a mobile environment is also a challenging
issue. Moreover, the sociological and psychological aspects of the integration
between data mining technology and our lifestyle are yet to be explored.
We need to develop the technology to offer the benefits of KDD in a ubiquitous fashion in such a way that the cost of ubiquitous presence is minimized. This workshop will focus on this emerging technology.
The topics of interest include, but are not limited to:
1. Theoretical foundation of UDM.
2. Methods and algorithms: Advanced algorithms for mobile and distributed
3. Data management issues, mark-up languages, and other data
representation techniques; integration with database applications for
4. Architectural issues: Architecture, control, security, and
5. Specialized mobile devices for UDM.
6. Experimental systems: Development of experimental systems, performance
7. Software agents and UDM: Agent based approaches in UDM, Agent
interaction---cooperation, collaboration, negotiation, organizational
8. Applications of UDM: Application in business, science, engineering,
medicine, and other disciplines.
9. Human-computer interaction: Human-computer interaction in UDM,
multi-user interaction in UDM.
10. Location management issues in UDM.
11. Technology for web-based applications of UDM.
June 8, 2001 --- Paper submission deadline
June 29, 2001 --- Paper acceptance notification
July 13, 2001 --- Paper camera-ready deadline
July 27, 2001 --- Workshop proceedings (camera- and Web-ready)
1. Introduction: Hillol Kargupta [9:00--9:05]
of the Net: A New Approach for a Context-Aware Health Club
S. Pirttikangas, J. Riekki, J. Kaartinen, J. Miettinen, S. Nissila, and J. Roning [9:05--9:40]
Spectrum-based Approach to Aggregate and Visualize Decision Trees for Mobile
H. Kargupta and B. Park [9:40--10:15]
10:15-10:45 Coffee Break
3. Invited Talk
Title: Web Intelligence – Mining the Web for Relevant Information:
Concepts & Applications
Jaideep Srivastava, University of Minnesotta [10:45--11:55]
approach to online Bayesian learning from multiple data streams
R. Chen, K. Sivakumar, and H. Kargupta [11:55--12:30]
12:30-14:30 Lunch Break
Clustering of Heterogeneous Distributed Databases
S. McClean, B. Scotney, K. Greer, and R. Pairceir [14:30--15:05]
Distribution is Part of the Semantics: A New Problem Class for Distributed
R. Wirth, M. Borth, and J. Hipp [15:05--15:40]
15:45-16:15 Coffee Break
Version Space Learning by Relevance Sharing
M. Sevenster and M. van Someren [16:15--16:50]
9. Open discussion, demos
All papers must be submitted to the following address:
School of Electrical Engineering and Computer Science
Washington State University
Pullman, WA 99164-2752
Phone: (509) 335-4969
Fax: (509) 335-3818
Electronic submission (postscript or pdf) is highly encouraged. For hard-copy submission, please send three (3) copies of the full paper to the above address.
1) be a maximum of 20 pages,
2) have a line spacing of 1.5,
3) use no smaller than a 12pt font, and
4) have at least a 1 inch margin on each side
The accepted papers will be published in the workshop proceedings. It
will be printed by the ECML/PKDD organizers and distributed among registered
participants of the workshop. In addition, there will be a joint Web-publication
of all the workshop proceedings after the PKDD-ECML conference.