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Towards Prescriptive Analyses of Querying Large Knowledge Graphs

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New Trends in Database and Information Systems (ADBIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1652))

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Abstract

Leveraging relational Big Data (BD) processing frameworks to process large-scale (RDF) graphs yields a great interest in optimizing query performance. Modern BD systems are yet complicated data systems, where the configurations notably affect the performance. Benchmarking different frameworks and configurations provides the community with best practices for better performance. However, most of these benchmarking efforts are classified as descriptive and diagnostic analytics. Moreover, there is no standard for comparing these benchmarks based on quantitative ranking techniques. In this paper, we discuss how our work fills this timely research gap. Particularly, we investigate how to enable prescriptive analytics via ranking functions (called “BenchRank”). We present a research plan that builds on the state-of-the-art benchmarking efforts in the area of querying large RDF graphs. Finally, we present our research results of the proposed plan.

M. Ragab—Supervised by Riccardo Tommasini, LIRIS Lab, INSA Lyon, France.

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Notes

  1. 1.

    The relational schema impacts query joins, partitioning techniques impact data shuffling, whilst storage formats impact physical execution plans.

  2. 2.

    We omit details about schema options (ST, VP, PT) and partitioning options (HP, SBP, PBP) due to space limits, however, still can be found in the project’s GitHub page: https://datasystemsgrouput.github.io/SPARKSQLRDFBenchmarking/.

  3. 3.

    Each configuration C has a rank according to its running time of the queries.

  4. 4.

    Kendall’s index is a common measure to compare the ordering of ranking functions.

  5. 5.

    Conformance and Coherence results [7] are omitted due to space limits.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Tartu University, Tartu, Estonia

    Mohamed Ragab

Authors
  1. Mohamed Ragab
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Correspondence to Mohamed Ragab .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  5. University of Genoa, Genoa, Italy

    Barbara Catania

  6. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas-Solar

  7. University of Calabria, Rende, Italy

    Ester Zumpano

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Ragab, M. (2022). Towards Prescriptive Analyses of Querying Large Knowledge Graphs. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_59

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  • DOI: https://doi.org/10.1007/978-3-031-15743-1_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15742-4

  • Online ISBN: 978-3-031-15743-1

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