CFP

                               Call for Papers
     IJCAI-01 Workshop on Knowledge Discovery from Distributed, Dynamic,
            Heterogeneous, Autonomous Data and Knowledge Sources
(http://www.cs.iastate.edu/~honavar/ijcai00workshop.html)

                             to be held at the
    International Joint Conference on Artificial Intelligence (ICAI-2001)
                  August 6, 2001 Seattle, Washington, USA.

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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.

Translating the advances in data acquisition, storage, and communication
technologies into fundamental gains in our ability to utilize the available
data for effective problem solving and decision making in respective domains
(e.g., data-driven knowledge discovery in biology, decision support systems
using disparate geospatial data sources) presents challenges in several
areas of artifiicial intelligence including machine learning, knowledge
representation, and multi-agent systems. Development of effective solutions
to this class of problems has to necessarily incorporate recent advances in
machine learning, knowledge representation, databases, distributed
computing, and related areas.

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).
   * 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).
   * 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: March 1, 2001.
   * Deadline for submission of position papers or abstracts: March 15,
     2001.
   * Notification of acceptance: March 30, 2001.
   * Deadline for receipt of camera-ready papers: April 21, 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 on the workshop web page.

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, and Kyseok
Shim. 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