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A probabilistic view of Datalog parallelization

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Database Theory — ICDT '95 (ICDT 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 893))

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Abstract

We explore an approach to developing Datalog parallelization strategies that aims at good expected rather than worst-case performance. To illustrate, we consider a very simple parallelization strategy that applies to all Datalog programs. We prove that this has very good expected performance under equal distribution of inputs. This is done using an extension of 0–1 laws adapted to this context. The analysis is confirmed by experimental results on randomly generated data.

This work was done while the author was affiliated with the Ecole Nationale Supérieure des Télécommunications (ENST), Paris, France, and supported in part by CAPES/MEC Brasil under grant #1245/90-13

Work performed in part while visiting ENST Paris, and supported in part by the NSF under grant IRI-9221268.

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Author information

Authors and Affiliations

  1. Depto. Informática, PUC-Rio, Rua Marquês de São Vicente 225, 22453-900, Rio de Janeiro, RJ, Brasil

    Sérgio Lifschitz

  2. Univ. of California at San Diego, CSE 0114, 92093-0114, La Jolla, CA, USA

    Victor Vianu

Authors
  1. Sérgio Lifschitz
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  2. Victor Vianu
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Georg Gottlob Moshe Y. Vardi

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© 1995 Springer-Verlag Berlin Heidelberg

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Lifschitz, S., Vianu, V. (1995). A probabilistic view of Datalog parallelization. In: Gottlob, G., Vardi, M.Y. (eds) Database Theory — ICDT '95. ICDT 1995. Lecture Notes in Computer Science, vol 893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58907-4_23

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  • DOI: https://doi.org/10.1007/3-540-58907-4_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58907-5

  • Online ISBN: 978-3-540-49136-1

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