The parallelization of a knowledge discovery system with hypergraph representation

Jennifer Seitzer, James P. Buckley, Yi Pan, Lee A. Adams

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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. ?

Original languageEnglish
Title of host publicationParallel and Distributed Processing - 15 IPDPS 2000 Workshops, Proceedings
EditorsJose Rolim
PublisherSpringer Verlag
Pages374-381
Number of pages8
ISBN (Print)354067442X, 9783540674429
DOIs
Publication statusPublished - 2000
Externally publishedYes
Event15 Workshops Held in Conjunction with the IEEE International Parallel and Distributed Processing Symposium, IPDPS 2000 - Cancun, Mexico
Duration: 1 May 20005 May 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1800 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15 Workshops Held in Conjunction with the IEEE International Parallel and Distributed Processing Symposium, IPDPS 2000
Country/TerritoryMexico
CityCancun
Period1/05/005/05/00

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