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A Comparative Study of Stream Reasoning Engines

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The Semantic Web (ESWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13870))

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Abstract

The diverse research efforts in recent years in the area of stream reasoning (SR) led to a wide range of SR engines. However, the lack of standardization and the diverse choices in SR (e.g., tuple-driven vs. time-driven engines, streaming all results vs. newly derived ones, ...) mean that real comparability among the engines is hardly given. A first step towards achieving comparability and standardization is the RSP-QL model, implemented in the RSP4J framework, which allows for describing and formalizing the semantics of SR engines. To further advance the state of the art in comparative research of stream reasoning, we present the results of a survey to quantify the in-use importance of several key performance indicators (KPIs) and features and compare SR engines along these KPIs with the CityBench and the CSRBench oracle. Our analysis shows that the two RSP4J implementations C-SPARQL2.0 and YASPER outperform the well-known C-SPARQL implementation in terms of performance and configurability. Our comparison against a naive SR extension of the incremental reasoning engine RDFox shows that SR engines still have potential for improvement. To avoid a costly integration of engines into several different benchmarking environments, we finally present a unifying interface, already aligned with the CityBench and CSRBench, for benchmarking SR engines.

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Notes

  1. 1.

    https://github.com/streamreasoning/csparql2.

  2. 2.

    https://project-hobbit.eu/.

  3. 3.

    https://github.com/streamreasoning/CSPARQL-engine.

  4. 4.

    https://github.com/streamreasoning/csparql2.

  5. 5.

    https://www.espertech.com/esper/.

  6. 6.

    https://jena.apache.org/.

  7. 7.

    https://www.oxfordsemantic.tech/.

  8. 8.

    The tick interval of 15 milliseconds was chosen experimentally as it was a good trade-off between low latency and not putting too much load on the engine.

  9. 9.

    https://github.com/SRrepo/CityBench-CSPARQL-RDFox/tree/master/src/org/java/aceis/utils/RDFox.

  10. 10.

    https://github.com/SRrepo/SurveyResults.

  11. 11.

    https://github.com/SRrepo/.

  12. 12.

    C-SPARQL2.0 commit number: f682cdc427d85594b39f9b4aa8d86e04833c8368,

    YASPER commit number: aea74443955e1ab3b95de7b0ef65f7c1dbd51d08,

    C-SPARQL commit number 4be27dd5ca23550da6bf7fb4e3420b0eb75132f0.

  13. 13.

    https://github.com/SRrepo/CSRBench-Aligned/blob/master/Parameters.md.

  14. 14.

    https://github.com/SRrepo/CityBench-Aligned.

  15. 15.

    https://github.com/SRrepo/CSRBench-Aligned.

  16. 16.

    https://github.com/SRrepo/CSPARQL-Running-Example-For-Unifying-Interface.

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Authors and Affiliations

  1. Ulm University, Helmholtzstraße 16, 89081, Ulm, Germany

    Nathan Gruber & Birte Glimm

Authors
  1. Nathan Gruber
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  2. Birte Glimm
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Correspondence to Nathan Gruber .

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Editors and Affiliations

  1. Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  2. University of London, London, UK

    Ernesto Jimenez-Ruiz

  3. Rensselaer Polytechnic Institute, Troy, MI, USA

    Jamie McCusker

  4. Universidade de Lisboa, Lisbon, Portugal

    Daniel Faria

  5. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  6. KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  7. EURECOM, Biot, France

    Raphael Troncy

  8. University of Mannheim, Mannheim, Germany

    Sven Hertling

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Gruber, N., Glimm, B. (2023). A Comparative Study of Stream Reasoning Engines. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_2

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  • DOI: https://doi.org/10.1007/978-3-031-33455-9_2

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