Seminaive Materialisation in DatalogMTL

TL;DR

This paper introduces a seminaive materialisation algorithm for DatalogMTL that reduces redundant computations, significantly improving the efficiency of temporal reasoning in data access and stream processing.

Contribution

It adapts the classical seminaive algorithm to DatalogMTL, minimizing redundant rule evaluations during materialisation.

Findings

Significant reduction in materialisation times observed.

Efficient handling of temporal rule instances.

Improved performance over naive evaluation strategies.

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 naive 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 seminaive 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 seminaive strategy for DatalogMTL is able to significantly reduce materialisation times.