CFP

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Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
August 20, 2000, Boston, MA, USA
Workshop Web Site: http://www.eecs.wsu.edu/~hillol/DKD/dpkd2000.html

Chairs:
Hillol Kargupta http://www.eecs.wsu.edu/~hillol hillol@eecs.wsu.edu
Joydeep Ghosh http://hercules.ece.utexas.edu/~ghosh ghosh@ece.utexas.edu
Philip Chan http://www.cs.fit.edu/~pkc pkc@cs.fit.edu
Vipin Kumar http://www-users.cs.umn.edu/~kumar kumar@cs.umn.edu
Zoran Obradovic http://www.eecs.wsu.edu/~zoran zoran@eecs.wsu.edu

Background Information

Information is most useful when it can be transformed into actionable
knowledge. Since the volume of data in most non-trivial processes is
typically large, the role of automated data analysis in modern information
processing applications is becoming increasingly important. The emerging
development of large, data rich distributed systems like the internet and
intranets has added another dimension to the problem---a plethora of
distributed information resources. Although the communication bandwidth is
increasing, by no means is it increasing at a rate that is even close to the
increase of available information. As a result, downloading large data sets
to a single site over limited bandwidth channels, followed by the
application of centralized data analysis algorithms may not be scalable for
the large, distributed data analysis applications of the future. Moreover,
this may not even be feasible because of security/privacy concerns, or
incompatibilities at the system or information  representation levels.
Examples include information retrieval in a mobile computing environment,
situation monitoring using large sensor networks, distributed real-time
advanced practice of telemedicine, and e-commerce applications. We need to
rethink our fundamental approach toward data analysis in these emerging new
environments. Distributed knowledge discovery (DKD) accepts the fact that
data may be inherently distributed among different loosely coupled sites
connected by a network and the sites may have heterogeneous data. It offers
techniques to discover new knowledge through distributed data analysis and
modeling using minimal communication of information. Design and development
of distributed data analysis algorithms, architectures and their
scalability, efficiency, ability to work with different kinds of data and
computing platforms such as handheld devices to web server farms, security,
human-computer interaction are some of the open research problems in DKD.


The first workshop on Distributed Data Mining, held at the Fourth
International Conference on Knowledge Discovery and Data Mining (1998)
brought the interested researchers and practitioners together and created an
environment for crystallizing the fast growing  field of DKD. The workshop
was focused on  the state-of-the-art DKD algorithms, systems, and
application related issues. Approximately 40 participants attended the
workshop. The workshop had 13 presentations, including 3 invited talks. It
was sponsored by the Intel Corporation and Magnify Inc. It resulted in a
book (edited by Hillol Kargupta and Philip Chan) to be published by AAAI/MIT
Press in early Summer of 2000. With the rapid growth in DKD technology both
in terms of research issues and real-world applications, we expect that the
second workshop will be even more successful. Further details about the
(1998) workshop can be found at http://www.eecs.wsu.edu/~hillol/ddm.html.


Today after about two years, this field has gained a large boost in
momentum. The number of researchers and practitioners working in this area
has nearly tripled. More research issues are introduced and the field is
starting to crystallize its shape. We believe it is now the  time for
hosting another workshop to discuss the state of the art research and
practice. This workshop will also extend its scope by including
complementary research in parallel data mining which is also an exciting
area and highly related to its distributed counterpart.

Workshop Description

The primary thrust of this workshop will be on knowledge discovery from
distributed data. However, high quality research on high performance
parallel data mining is also relevant to the workshop. The proposed workshop
will provide a platform to  discuss both theoretical and applied research
issues in distributed and parallel  knowledge discovery (DPKD). The topics
of interest include, but are not limited to:


   1) Theoretical foundation of DPKD.

   2) Methods and algorithms: Advanced distributed and parallel data
      analysis and knowledge discovery algorithms.

   3) Architectural issues: Architecture, control, security, and
      communication issues in DPKD.

   4) Distributed data analysis in mobile computing environments.

   5) Experimental systems: Large experimental systems, performance design
      issues.

   6) Software agents and DPKD: Agent based approaches in DPKD. Agent
      interaction: cooperation, collaboration, negotiation, organizational
      behavior.

   7) Distributed and Parallel knowledge discovery from spatial data.

   8) Applications of DPKD: Application in business, science, engineering,
      medicine, and other disciplines.

   9) Human-computer interaction in DPKD: Human-computer interaction in
      DPKD, multi-user interaction in DPKD.

  10) Distributed Data analysis on the Internet.


Important Dates

March 1, 2000:  Workshop Call for Papers.
May 15, 2000:  Papers Due.
June 15, 2000:  Acceptance Notification.
July 15, 2000:  Revision Due.
August 20, 2000:  Workshop.

for additional information see http://www.eecs.wsu.edu/~hillol/DKD/dpkd2000.html

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