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European Knowledge Acquisition Workshop

EKAW 2018: Knowledge Engineering and Knowledge Management pp 3–18Cite as

An Empirical Evaluation of RDF Graph Partitioning Techniques

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11313))

Abstract

With the significant growth of RDF data sources in both numbers and volume comes the need to improve the scalability of RDF storage and querying solutions. Current implementations employ various RDF graph partitioning techniques. However, choosing the most suitable partitioning for a given RDF graph and application is not a trivial task. To the best of our knowledge, no detailed empirical evaluation exists to evaluate the performance of these techniques. In this work, we present an empirical evaluation of RDF graph partitioning techniques applied to real-world RDF data sets and benchmark queries. We evaluate the selected RDF graph partitioning techniques in terms of their partitioning time, partitioning imbalance (in sizes), and query run time performances achieved, based on real-world data sets and queries selected using the FEASIBLE benchmark generation framework.

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Notes

  1. 1.

    TCGA: http://tcga.deri.ie/.

  2. 2.

    UniProt: http://www.uniprot.org/statistics/.

  3. 3.

    http://glaros.dtc.umn.edu/gkhome/metis/metis/download.

  4. 4.

    Please see T-Test tab of the excel sheet goo.gl/fxa4cJ.

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Acknowledgements

This work was supported by the H2020 project HOBBIT (no. 688227).

Author information

Authors and Affiliations

  1. AKSW, Leipzig, Germany

    Adnan Akhter, Axel-Cyrille Ngomo Ngonga & Muhammad Saleem

  2. University of Paderborn, Paderborn, Germany

    Axel-Cyrille Ngomo Ngonga

Authors
  1. Adnan Akhter
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  2. Axel-Cyrille Ngomo Ngonga
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  3. Muhammad Saleem
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Corresponding author

Correspondence to Adnan Akhter .

Editor information

Editors and Affiliations

  1. Université Côte d’Azur, CNRS, Inria, I3S, Sophia Antipolis, France

    Catherine Faron Zucker

  2. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  3. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Amedeo Napoli

  4. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Yannick Toussaint

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Akhter, A., Ngomo Ngonga, AC., Saleem, M. (2018). An Empirical Evaluation of RDF Graph Partitioning Techniques. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds) Knowledge Engineering and Knowledge Management. EKAW 2018. Lecture Notes in Computer Science(), vol 11313. Springer, Cham. https://doi.org/10.1007/978-3-030-03667-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-03667-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03666-9

  • Online ISBN: 978-3-030-03667-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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