  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.
