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International Semantic Web Conference

ISWC 2020: The Semantic Web – ISWC 2020 pp 238–257Cite as

Cost- and Robustness-Based Query Optimization for Linked Data Fragments

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12506))

Abstract

Client-side SPARQL query processing enables evaluating queries over RDF datasets published on the Web without producing high loads on the data providers’ servers. Triple Pattern Fragment (TPF) servers provide means to publish highly available RDF data on the Web and clients to evaluate SPARQL queries over them have been proposed. For clients to devise efficient query plans that minimize both the number of requests submitted to the server as well as the overall execution time, it is key to accurately estimate join cardinalities to appropriately place physical join operators. However, collecting accurate and fine-grained statistics from remote sources is a challenging task, and clients typically rely on the metadata provided by the TPF server. Addressing this shortcoming, we propose CROP, a cost- and robust-based query optimizer to devise efficient plans combining both cost and robustness of query plans. The idea of robustness is determining the impact of join cardinality estimation errors on the cost of a query plan and to avoid plans where this impact is very high. In our experimental study, we show that our concept of robustness complements the cost model and improves the efficiency of query plans. Additionally, we show that our approach outperforms existing TPF clients in terms of overall runtime and number of requests.

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Notes

  1. 1.

    http://fragments.dbpedia.org/2014/en.

  2. 2.

    https://github.com/comunica/comunica/tree/master/packages/actor-init-sparql.

  3. 3.

    https://github.com/maribelacosta/nlde.

  4. 4.

    \(\llbracket \cdot \rrbracket \) denote Iverson brackets that evaluate to 1 if its logical proposition is true and to 0 otherwise.

  5. 5.

    Complex queries do not contain placeholders, leading to one distinct query in C1, C2, and C3.

  6. 6.

    http://www.rdfhdt.org/datasets/.

  7. 7.

    https://github.com/Lars-H/crop_analysis.

  8. 8.

    https://github.com/Lars-H/crop.

  9. 9.

    https://github.com/LinkedDataFragments/Server.js.

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Acknowledgement

This work is funded by the German BMBF in QUOCA, FKZ 01IS17042.

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Authors and Affiliations

  1. Institute AIFB, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Lars Heling & Maribel Acosta

Authors
  1. Lars Heling
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  2. Maribel Acosta
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Corresponding author

Correspondence to Lars Heling .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Heling, L., Acosta, M. (2020). Cost- and Robustness-Based Query Optimization for Linked Data Fragments. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_14

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  • DOI: https://doi.org/10.1007/978-3-030-62419-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62418-7

  • Online ISBN: 978-3-030-62419-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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