MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
Dingmin Wang, Pan Hu, Przemys{\l}aw Andrzej Wa{\l}\k{e}ga, Bernardo, Cuenca Grau

TL;DR
MeTeoR is a scalable reasoning system for DatalogMTL that combines materialisation and automata techniques, enabling efficient processing of large-scale temporal datasets with complex rules.
Contribution
The paper introduces MeTeoR, a novel practical reasoning approach for DatalogMTL that improves scalability and efficiency using combined techniques.
Findings
MeTeoR handles tens of millions of temporal facts efficiently.
It outperforms existing methods on benchmark datasets.
Scalable reasoning over complex temporal rules is achievable.
Abstract
DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in DatalogMTL is, however, of high computational complexity, making implementation challenging and hindering its adoption in applications. In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. We have implemented this approach in a reasoner called MeTeoR and evaluated its performance using a temporal extension of the Lehigh University Benchmark and a benchmark based on real-world meteorological data. Our experiments show that MeTeoR is a scalable system which enables…
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Taxonomy
TopicsSemantic Web and Ontologies · Advanced Database Systems and Queries · Data Management and Algorithms
