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The Parallelization of a Knowledge Discovery System with Hypergraph Representation

  • Jennifer Seitzer
  • James P. Buckley
  • Yi Pan
  • Lee A. Adams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1800)

Abstract

Knowledge discovery is a time-consuming and space intensive endeavor. By distributing such an endeavor, we can diminish both time and space. System INDED(pronounced “indeed”) is an inductive implementation that performs rule discovery using the techniques of inductive logic programming and accumulates and handles knowledge using a deductive nonmonotonic reasoning engine. We present four schemes of transforming this large serial inductive logic programming (ILP) knowledge-based discovery system into a distributed ILP discovery system running on a Beowulf cluster. We also present our data partitioning algorithm based on locality used to accomplish the data decomposition used in the scenarios.

Keywords

Association Rule Logic Program Logic Programming Inductive Logic Programming Stable Model Semantic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Jennifer Seitzer
    • 1
  • James P. Buckley
    • 1
  • Yi Pan
    • 1
  • Lee A. Adams
    • 1
  1. 1.Department of Computer ScienceUniversity of DaytonDayton

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