• Salvatore Orlando, Ca' Foscari University of Venice (
  • K. Sivakumar, Washington State University (



    Gagan Agrawal, Ohio State University
    Rong Chen, University of Pennsylvania
    Chris Giannella, University of Maryland Baltimore County
    Sara Graves, The University of Alabama at Huntsville
    Matthias Klusch, German Research Center for Artificial Intelligence 
    Shonali Krishnaswamy
    , Monash University
    Michael May, Fraunhofer Institute for Autonomous Intelligent Systems
    Hoony Park
    , Oak Ridge National Laboratory
    Raffaele Perego, Consiglio Nazionale delle Ricerche
    Assaf Schuster, Technion
    Parthasarathy Srinivasan, Ohio State University
    Ashok Srivastava, NASA Ames Research Center
    Vaidy Sunderam, Emory University
    Domenico Talia, UniversitÓ della Calabria
    Ran Wolff, Technion
    Mohammed Zaki, Rensselaer Polytechnic Institute





  • 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

    8th International Workshop on High Performance and Distributed Mining (HPDM'05)

    April 23, 2005

    in conjunction with

    Fifth International SIAM Conference on Data Mining

    Call For Papers

    Workshop Schedule

    Workshop History: This is the 8th workshop on this theme held annually. Traditionally, the workshop has been held along-side the SIAM datamining (SDM) conference, even if the first four editions were organized in conjunction with IPDPS, and were held at Orlando (HPDM'98), San Juan (HPDM'99), Cancun (HPDM'00) and San Francisco (PDDM'01). Over the last three years the workshop has had invited papers in the areas of mobile and location-aware data mining issues (HPDM:RLM'02), pervasive and stream datamining (HPDM:PDS'03), and grid data mining ( HPDM:GRID'04).

    Over the years the definition of high performance computing has taken on various forms as a function of the types of technical and creative uses and the underlying semantics of the applications driving them. Traditional definitions often refer to the problem of using high end parallel computers to meet the need of scientific applications. However, high performance computing can also include the need for fast sequential algorithms that target memory and I/O performance. The last decade has seen the growth and importance of grid computing where resources and data are physically distributed. This has led to the development of high performance distributed algorithms over the computational grid, where privacy, security, and resource discovery are all important issues. This year the workshop welcomes papers on all aspects of high performance data mining.

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

    • Grid-based data mining algorithms and systems
    • Distributed techniques for incremental, exploratory and interactive mining
    • Distributed techniques for security, privacy preserving data mining
    • Peer-to-Peer Data Mining
    • High performance data stream mining and management
    • Resource and location-aware mining algorithms
    • Data mining in mobile environments
    • Theoretical foundations for resource-aware mining in a mobile, streaming and/or distributed environment.
    • Systems support for resource and location aware data mining
    • Efficient, scalable, disk-based, parallel and distributed algorithms for large-scale data mining and pre-processing and post-processing tasks
    • Parallel or distributed frameworks for stream management, KDD systems, and parallel or distributed mining
    • Applications of parallel and distributed datamining (PDDM) in business, science, engineering, medicine, and other disciplines.

      Important Dates:
    • Paper Submissions due:
      January 17, 2005
    • Notification to authors:
      Feb 17, 2005
    • Final papers due:
      February 27, 2005

    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