Optimised Storage for Datalog Reasoning
This work addresses memory inefficiency in Datalog reasoning for applications with large datasets and complex rules, offering an incremental improvement over existing methods.
The paper tackles the problem of high memory consumption in Datalog reasoning by proposing a framework that integrates optimized storage schemes with standard materialization algorithms, resulting in significant memory reductions, sometimes by orders of magnitude, while maintaining competitive query times.
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.