Background

Recent advances in sensor, high throughput data acquisition, and digital information storage technologies, have made it possible to acquire and store large volumes of data in digital form. Advances in computers and communications, the Internet, and mobile computing have made it possible for scientists and decision makers, at least in principle, to access and utilize this data for data-driven knowledge acquisition and decision making in the respective domains. Examples of such domains include bioinformatics, monitoring and control of complex, dynamic, distributed systems (e.g., communication networks, power systems), among others. Despite the diversity of these domains, they share several common characteristics:

• Different data sources often provide different types of data (e.g., signals from sensors, relational data, text, images, macromolecular (DNA and protein) sequences, protein structures, simulations). This calls for sophisticated tools for selective and context-sensitive information extraction and information fusion. Such tools have to be able to bridge the gap in structure and semantics of the respective data and knowledge sources (e.g., using domain-specific ontologies).
• Data Repositories of interest are physically distributed. Given the large amounts of data that is being gathered and stored at these repositories, and the fact that users are typically interested not in the raw data, but in results of analysis of the data in a given context, it is desirable to process the data in a distributed fashion wherever the data is located and selectively transmit the results of analysis. This calls for efficient and scalable analysis tools (e.g. data mining algorithms and decision making algorithms) with provable performance guarantees in a distributed setting.
• Data sources are often autonomous and the nature of access to data that is available is often restricted due to privacy and security considerations. Thus, users have a limited view of the data (e.g., in the form of statistical summaries or results of an agreed-upon set of operations). Thus there is a need for systematic analysis of the information requirements of data analysis or decision making algorithms in such environments.
• Data sources are dynamic. Given the large amounts of data that need to be processed, this calls for efficient incremental or cumulative algorithms that can update the results of analysis (e.g., a hypthesis generated by a data mining algorithm).
• The goals and consequently information needs of users as well as the data sources can change over time. This calls for development of information extraction and fusion algorithms and data mining algorithms that can dynamically adjust to shifting goals and changing constraints.

Participation

The workshop is open to all members of the AI community. However, the number of participants is strictly limited. Consequently, authors of accepted papers will be given priority in terms of attendance. All workshop participants must register for the IJCAI conference. The organizers will make a concerted effort to ensure a good mix of established researchers, graduate students and junior researchers, as well as industrial participants.

Topics of Interest

We invite full papers, extended abstracts, or position papers on all aspects of knowledge discovery from distributed, dynamic, heterogeneous, autonomous data and knowledge sources, including, but not limited to, the following topics:

• Learning from Distributed Data Sources (types of data fragmentation, alternative formulations of distributed learning problem, information requirements of distributed learning, distributed learning algorithms, performance measures, efficiency and scalability issues).
• Learning from Dynamic Data Sources (alternative formulations of the incremental and cumulative learning problems, information requirements of incremental learning, incremental learning algorithms, performance measures, efficiency and scalability issues).
• Customizable and Context-Sensitive Information Extraction and Fusion from Distributed, Heterogeneous Data Sources (traditional database techniques for data integration (e.g., views), wrapper and mediator based techniques for handling unstructured and semistructured data, automated generation of domain specific information extraction and information fusion operators, ontologies for information integration).
• Architectures and Systems (software agents, multi-agent systems, collaborative learning, collaborative decision-making) for knowledge discovery from heterogeneous, distributed, dynamic, autonomous data and knowledge sources
• Data and Knowledge Visualization and Decision-Making in Distributed Environments
• Applications in internet-based information systems, geo-spatial information systems, communication systems, power grid, information assurance, scientific discovery (e.g., in bioinformatics).

Important Dates and Deadlines

Some important dates are:

• Deadline for submission of full papers: April 1, 2001.
• Deadline for submission of position papers or abstracts: April 1, 2001.
• Notification of acceptance: April 15, 2001.
• Deadline for receipt of camera-ready papers: April 30, 2001
• Workshop: August 6, 2001.

Instructions for Authors

Postscript or PDF versions of the papers, no more than 10 pages long, (including figures, tables, and references), should be submitted electronically to honavar@cs.iastate.edu. Accepted papers will be allocated 10 pages in the proceedings (long papers) or 5 pages in the proceedings (extended abstracts or position papers). Formatting guidelines for the preparation of camera-ready versions can be found here.

In those rare instances where authors might be unable to submit postscript versions of their papers electronically, we will try to accomodate them.

Each paper will be rigorously refereed by at least 2 reviewers for technical soundness, originality, and clarity of presentation.

Workshop Organizers

The workshop will be organized by Vasant Honavar, Lee Giles, Kyseok Shim, Kristina Lerman, and Yannis Labrou. Vasant Honavar will serve as the primary contact.

Dr. Vasant Honavar
Department of Computer Science
226 Atanasoff Hall
Iowa State University
Ames, IA 50011
honavar@cs.iastate.edu

Dr. Lee Giles
School of Information Sciences and Technology
Pennsylvania State University
504 Rider Building
120 South Burrowes St.
University Park, PA 16801-3857
Giles@ist.psu.edu

Dr. Kyuseok Shim
Computer Science Department
Korea Advanced Institute of Science and Technology
373-1 Kusong-dong, Yusong-gu
TAEJON 305-701, KOREA
shim@cs.kaist.ac.kr

Dr. Kristina Lerman
Information Sciences Institute
4676 Admiralty Way
Marina del Rey, CA 90292-6695
lerman@isi.edu

Dr. Yannis Labrou
Computer Science and Electrical Engineering Department
University of Maryland, Baltimore County
ECS Building, Room 210
1000 Hilltop Circle
Baltimore, MD 21250.
jklabrou@cs.umbc.edu

Please direct any corrections to this page to:
Dr. Vasant Honavar
Artificial Intelligence Research Laboratory
Department of Computer Science
Iowa State University
Atanasoff Hall, Ames, IA 50011-1040 USA
phone: +1-515-294-1098, +1-515-294-4377; fax: +1-515-294-0258