Modular Materialisation of Datalog Programs

TL;DR

This paper introduces a modular framework for efficient datalog materialisation that combines specialized algorithms with the seminaive approach, significantly improving performance on arbitrary datalog programs.

Contribution

It proposes a modular approach to datalog materialisation that integrates custom algorithms for specific rule types with the seminaive algorithm for general rules.

Findings

Framework handles arbitrary datalog programs effectively.

Empirical results show significant performance improvements.

Specialized algorithms for transitive closures enhance efficiency.

Abstract

The semina\"ive algorithm can materialise all consequences of arbitrary datalog rules, and it also forms the basis for incremental algorithms that update a materialisation as the input facts change. Certain (combinations of) rules, however, can be handled much more efficiently using custom algorithms. To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for materialisation computation and its maintenance. We split a datalog program into modules that can be handled using specialised algorithms, and handle the remaining rules using the semina\"ive algorithm. We also present two algorithms for computing the transitive and the symmetric-transitive closure of a relation that can be used within our framework. Finally, we show empirically that our framework can handle arbitrary datalog programs while outperforming existing approaches, often by orders of magnitude.