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Strategies to Parallelize ILP Systems

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Inductive Logic Programming (ILP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3625))

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Abstract

It is well known by Inductive Logic Programming (ILP) practioners that ILP systems usually take a long time to find valuable models (theories). The problem is specially critical for large datasets, preventing ILP systems to scale up to larger applications. One approach to reduce the execution time has been the parallelization of ILP systems. In this paper we overview the state-of-the-art on parallel ILP implementations and present work on the evaluation of some major parallelization strategies for ILP. Conclusions about the applicability of each strategy are presented.

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Author information

Authors and Affiliations

  1. DCC-FC & LIACC, Universidade do Porto, R. do Campo Alegre 823, 4150-180, Porto, Portugal

    Nuno A. Fonseca & Fernando Silva

  2. Faculdade de Engenharia & LIACC, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal

    Rui Camacho

Authors
  1. Nuno A. Fonseca
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  2. Fernando Silva
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  3. Rui Camacho
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Editor information

Editors and Affiliations

  1. Institut für Informatik I12, Technische Universität München, Boltzmannstr. 3, D-85748, Garching b. München, Germany

    Stefan Kramer

  2. Department of Computer Science, University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

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Fonseca, N.A., Silva, F., Camacho, R. (2005). Strategies to Parallelize ILP Systems. In: Kramer, S., Pfahringer, B. (eds) Inductive Logic Programming. ILP 2005. Lecture Notes in Computer Science(), vol 3625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536314_9

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  • DOI: https://doi.org/10.1007/11536314_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28177-1

  • Online ISBN: 978-3-540-31851-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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