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Partitioning Templates for RDF

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Advances in Databases and Information Systems (ADBIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9282))

Abstract

In this paper, we present an RDF data distribution approach which overcomes the shortcomings of the current solutions in order to scale RDF storage both with the volume of data and query requests. We apply a workload-aware method that identifies frequent patterns accessed by queries in order to keep related data in the same partition. In order to avoid exhaustive analysis on large datasets, a summarized view of the datasets is considered to deploy our reasoning through partitioning templates for data items in an RDF structure. An experimental study shows that our method scales well and is effective to improve the overall performance by decreasing the amount of message passing among servers, compared to alternative data distribution approaches for RDF.

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Notes

  1. 1.

    http://wiki.dbpedia.org/Datasets.

  2. 2.

    http://www.w3.org/wiki/LargeTripleStores.

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Acknowledgments

This work was partially supported by CAPES, CNPq, Fundação Araucária and by AWS in Education.

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Authors and Affiliations

  1. Universidade Do Estado de Santa Catarina - UDESC, Joinville, Santa Catarina, 89.219-710, Brazil

    Rebeca Schroeder

  2. Universidade Federal Do Paraná- UFPR, Curitiba, Paranà, 81531-990, Brazil

    Carmem S. Hara

Authors
  1. Rebeca Schroeder
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  2. Carmem S. Hara
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Corresponding author

Correspondence to Rebeca Schroeder .

Editor information

Editors and Affiliations

  1. Poznan University of Technology, Poznán, Poland

    Morzy Tadeusz

  2. INRIA, Montpellier, France

    Patrick Valduriez

  3. Teleport 2, LIAS/ISAE-ENSMA, Poitiers, France

    Ladjel Bellatreche

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Schroeder, R., Hara, C.S. (2015). Partitioning Templates for RDF. In: Tadeusz, M., Valduriez, P., Bellatreche, L. (eds) Advances in Databases and Information Systems. ADBIS 2015. Lecture Notes in Computer Science(), vol 9282. Springer, Cham. https://doi.org/10.1007/978-3-319-23135-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-23135-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23134-1

  • Online ISBN: 978-3-319-23135-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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