Seminaïve Materialisation in DatalogMTL

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 de...

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Bibliographic Details
Published inRules and Reasoning pp. 183 - 197
Main Authors Wang, Dingmin, Wałęga, Przemysław Andrzej, Cuenca Grau, Bernardo
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
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Summary: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.
ISBN:3031215400
9783031215407
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-031-21541-4_12