• Gagan Agrawal  
  • Ohio State University
    ( )
  • Domenico Talia  
  • University of Calibria, Italy  
  • Sara Graves 
  • University of Alabama, Huntsville  


  • H. Andrade, University of Maryland
  • P. Brezany, University of Vienna
  • E. Bertino, DSI, University of Milan
  • I. Cruz, University of Illinois, Chicago
  • M. Dikaiakos, University of Cyprus
  • R. Grossman, University of Illinois, Chicago
  • V. Kumar, University of Minnesota
  • R. Musick, iKuni Inc.
  • G. Ostrouchov, Oak Ridge National Labs
  • S. Orlando, University of Venice
  • Y. Pan, Georgia State University
  • R. Ramachandran, University of Alabama, Huntsville
  • S. Ranka, University of Florida
  • J. Rushing, University of Alabama, Huntsville
  • K. Sivakumar, Washington State University
  • A. Srivastava, NASA Ames
  • V. Sunderam, Emory University
  • G. Williams, National University of Australia
  • M. Zaki, RPI
  • N. Zhong, Maebashi Inst. of Technology

  • Hillol Kargupta, University of Maryland, Baltimore County
  • Vipin Kumar, University of Minnesota
  • Srinivasan Parthasarathy, Ohio State University
  • David Skillicorn, Queens University
  • Mohammed Zaki, RPI
  • HPDM: High Performance and Distributed Mining

    7th International Workshop on High Performance and Distributed Mining:

    April 2004

    in conjunction with

    Fourth International SIAM Conference on Data Mining

    Tentative Schedule

    Workshop History: This is the 7th workshop on this theme held annually. The first four held in conjunction with IPDPS were held at Orlando ( HPDM'98), San Juan ( HPDM'99), Cancun (HPDM'00). and San Francisco (PDDM01). Last two years' workshop were held along-side the SIAM Data Mining conference. This year's workshop, while continuing its focus on traditional areas of high performance data mining, has a special focus on grid-based and distributed data mining.

    This workshop will focus on the emerging field of high performance, distributed, and grid-based mining. With the emergence of grid architectures and standards, many efforts are being initiated on grid-based and distributed mining. This brings in new challenges in applying and developing distributed and high-performance algorithms. Privacy, security, and resource discovery are all important issues. Interoperability with grid standards is another issues. This area also has synergy with the growing area of mining in mobile and sensor network environments.

    Topics of interest include (but are not limited to):

    • Distributed and Grid-based data mining algorithms.
    • Systems and tools for data mining and exploration in grid-based and distributed environments.
    • Standards and architectures for grid-based data mining.
    • Privacy and security considerations in distributed data mining.
    • P2P data mining.
    • Resource-aware data mining.
    • Data mining in mobile / sensor network environments.
    • Data stream mining and management.
    • Efficient, scalable, disk-based, parallel and distributed algorithms for large-scale data mining.
    • Parallel and distributed techniques for incremental, exploratory and interactive mining.
    • Frameworks for parallel data mining.
    • Applications of parallel, distributed, grid-based, and mobile data mining.
    • Theoretical foundation of parallel, distributed, and grid-based data mining.

      Important Dates:
    • Paper Submissions due :
      January 15, 2004
    • Notification to authors:
      Feb 20, 2004.
    • Final papers due:
      March 10, 2004.

    Submission Information: We invite papers treating the above topics in one of many ways. The papers could describe new results, give overview or experiences with existing systems, describe new and emerging applications, present work in progress where interesting insights have been gained, or critically survey existing work. The papers should not exceed 3000 words. This is roughly equal to 6 pages of single spaced text with 10 pt format. You can submit by emailing the PS or PDF file to .