WORKSHOP CO-CHAIRS:
  • S. Parthasarathy 

    • Ohio State University
    (srini@cis.ohio-state.edu )
  • H. Kargupta 

    • University of Maryland (Baltimore County Campus)  
    (hillol@csee.umbc.edu)
  • V. Kumar 

    • University of Minnesota 
    (kumar@cs.umn.edu)
  • D. Skillicorn

    • Queens University, Canada 
    (skill@cs.queensu.ca) 
  • M.J. Zaki

    • Rensselaer Polytechnic Institute
    (zaki@cs.rpi.edu ) 

    PROGRAM COMMITTEE:

  • E. Bertino, DSI, University of Milan, Italy 
  • D. Cheung, University of Hong Kong, Hong Kong 
  • A. Choudhary, Northwestern University, USA 
  • Alex A. Freitas, PUC-PR (Pontifical Catholic University of Parana), Brazil 
  • J. Gehrke, Cornell University, USA 
  • R. Grossman, University of Illinois-Chicago, USA  
  • Y. Guo, Imperial College, UK 
  • Ron Musick, iKuni Inc., USA  
  • M. May, GMD, Germany. 
  • S. McClean, University of Ulster, N. Ireland, UK. 
  • E. Neuhold, GMD, Germany. 
  • Y. Pan, Georgia State University, USA  
  • N. Samatova, Oak Ridge National Labs, USA  
  • P. Scheuermann, Northwestern University, USA  
  • K. Sivakumar, Washington State University, USA  
  • N. Soparkar, Univeristy of Michigan, USA  
  • D. Talia, DEIS, University of Calabria, Italy  
  • G. Williams, CSIRO, Aust. Nat. Univ., Australia
  • R. Wirth, Daimler Chrysler, Germany
  • A. Zomaya, University of Western Australia, Australia
    		•

    HPDM: RLM 2002

    (advance program now available here )

    5th International Workshop on High Performance Data Mining:
    Resource and Location aware Mining (HPDM:RLM'02)
    April, 2002, Washington, USA

    in conjunction with

    Second International SIAM Conference on Data Mining

    Workshop History: This is the 5th 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). This years workshop in addition to retaining the same name, has a special focus on mobile and location-aware data mining issues.

    Increasingly the datasets used for data mining are physically distributed. This may be because their size prevents them being gathered in one location. It may also be because of legal or social restrictions that prevent data gathered in one jurisdiction from being moved to another. The focus of the workshop is data mining techniques that are aware of the location and resource needs of datasets. This may include: partitioning datasets for faster processing, knowledge discovery in distributed datasets without gathering the raw data, data mining when the data itself may change location unpredictably, and data mining in grid and mobile settings where the processing may occur independently of the user's location or computing power.

    Special focus topics of interest include but are not limited to:

    • New resource and location-aware algorithms for common data mining tasks such as association mining, sequence mining, classification and clustering.
    • Location and data-sensitive pre-processing and post-processing operations like sampling, dimensionality reduction, rule pruning and visualization.
    • Data mining in mobile environments.
    • Grid-based data mining.
    • Agent-based approaches for location-aware and distributed data mining.
    • Theoretical foundations of resource-aware data mining.
    • Systems support for resource and location aware data mining.

    In addition to the above special topics of interest we also invite papers along more traditional parallel and distributed data mining (PDDM) lines such as:

    • Efficient, scalable, disk-based, parallel and distributed algorithms for large-scale data mining and pre-procesing and post-processing tasks.
    • Parallel or distributed techniques for incremental, exploratory and interactive mining.
    • Meta-mining, coping with distributed and/or heterogeneous datasets.
    • Integration of mining with parallel/distributed databases and datawarehouses.
    • Frameworks for KDD systems, and parallel or distributed mining.
    • Agent based approaches for PDDM.
    • Applications of PDDM in business, science, engineering, medicine, and other disciplines. 
    • Theoretical foundation of PDDM. 
    		  Important Dates:
    • Paper Submissions due:
      January 18 2002.
    • Notification to authors:
      Febrary 12 2002.
    • Final papers due:
      Febrary 26 2002.

    Submission Information: Authors are instructed to submit articles that meet the following criteria:

    • No more than 20 double spaced pages excluding bibliography.
    • At least 11pt. font.
    • One title page (not included in the above 20) containing:
      • the title
      • names and affiliations of all authors
      • contact author information
      • abstract of no more than 100 words
    • PS or PDF format only.
    Papers that do not meet the above criteria may not be reviewed. Online submissions are encouraged and can be accomplished by clicking here . If you wish to submit earlier you can submit by emailing the PS or PDF file to srini@cis.ohio-state.edu . Hardcopy submissions may be sent to:
      Dr. Srinivasan Parthasarathy
      DL 395, 2015 Neil Ave.,
      Ohio State University,
      Columbus Ohio-43210
    Please be aware that hardcopy submissions must arrive at the above address by the due date in order to be considered by the program committee.

    Additional Information:

    • Following the tradition in previous workshops in addition to contributed papers there will be one or two invited talks and/or a panel discusssion relevant to the theme of the workshop.
    • Program committee members will be instructed to consider work-in-progress papers with novel ideas and such papers are welcomed by the co-chairs.
    Maintained by: Srinivasan Parthasarathy <srini@cis.ohio-state.edu>
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