Optimised Storage for Datalog Reasoning

TL;DR

This paper introduces a framework for optimized storage schemes in Datalog reasoning that significantly reduces memory usage while maintaining efficient query answering, especially for transitive and union rules.

Contribution

It proposes a general framework for integrating compact data structures with materialisation algorithms, focusing on transitive and union rules, improving memory efficiency.

Findings

Significant reduction in memory consumption, sometimes by orders of magnitude.

Maintains competitive query answering times.

Effective for complex rule combinations in Datalog reasoning.

Abstract

Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts. However, storing all materialised facts may be infeasible in practice, especially when the rules are complex and the given set of facts is large. We observe that for certain combinations of rules, there exist data structures that compactly represent the reasoning result and can be efficiently queried when necessary. In this paper, we present a general framework that allows for the integration of such optimised storage schemes with standard materialisation algorithms. Moreover, we devise optimised storage schemes targeting at transitive rules and union rules, two types of (combination of) rules that commonly occur in practice. Our experimental evaluation shows that our approach significantly improves memory consumption, sometimes by orders of magnitude, while remaining competitive in terms of query answering time.