MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators

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 reasoning over complex temporal rules and datasets involving tens of millions of temporal facts.