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Seminaïve Materialisation in DatalogMTL

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Rules and Reasoning (RuleML+RR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13752))

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Abstract

DatalogMTL is an extension of Datalog with metric temporal operators that has found applications in temporal ontology-based data access and query answering, as well as in stream reasoning. Practical algorithms for DatalogMTL are reliant on materialisation-based reasoning, where temporal facts are derived in a forward chaining manner in successive rounds of rule applications. Current materialisation-based procedures are, however, based on a naïve evaluation strategy, where the main source of inefficiency stems from redundant computations. In this paper, we propose a materialisation-based procedure which, analogously to the classical seminaïve algorithm in Datalog, aims at minimising redundant computation by ensuring that each temporal rule instance is considered at most once during the execution of the algorithm. Our experiments show that our optimised seminaïve strategy for DatalogMTL is able to significantly reduce materialisation times.

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Notes

  1. 1.

    https://github.com/wdimmy/MeTeoR/tree/main/experiments/RR2022.

  2. 2.

    For presentation convenience, we disallow \(\bot \) in rule heads, which ensures satisfiability and allows us to focus on the materialisation process itself.

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Acknowledgments

This work was supported by the EPSRC project OASIS (EP/S032347/1), the EPSRC project UK FIRES (EP/S019111/1), and the SIRIUS Centre for Scalable Data Access, and Samsung Research UK.

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Authors and Affiliations

  1. Department of Computer Science, University of Oxford, Oxford, UK

    Dingmin Wang, Przemysław Andrzej Wałęga & Bernardo Cuenca Grau

Authors
  1. Dingmin Wang
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  2. Przemysław Andrzej Wałęga
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  3. Bernardo Cuenca Grau
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Correspondence to Dingmin Wang .

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Editors and Affiliations

  1. Singapore Management University, Singapore, Singapore

    Guido Governatori

  2. Dresden University of Technology, Dresden, Germany

    Anni-Yasmin Turhan

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Wang, D., Wałęga, P.A., Cuenca Grau, B. (2022). Seminaïve Materialisation in DatalogMTL. In: Governatori, G., Turhan, AY. (eds) Rules and Reasoning. RuleML+RR 2022. Lecture Notes in Computer Science, vol 13752. Springer, Cham. https://doi.org/10.1007/978-3-031-21541-4_12

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  • DOI: https://doi.org/10.1007/978-3-031-21541-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21540-7

  • Online ISBN: 978-3-031-21541-4

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