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.

这篇论文提出了一个通用框架，将优化的存储方案与标准物质化算法集成，针对常见的转换规则和联合规则，通过优化的存储方案显著改进了内存消耗，有时候提升几个数量级，同时在查询响应时间方面保持竞争力。

数据逻辑推理的优化存储

Optimised Storage for Datalog Reasoning

Datalog reasoning based on the semina\"ive evaluation strategy evaluates
rules using traditional join plans, which often leads to redundancy and
inefficiency in practice, especially when the rules are complex. Hypertree
decompositions help identify efficient query plans and reduce similar
redundancy in query answering. However, it is unclear how this can be applied
to materialisation and incremental reasoning with recursive Datalog programs.
Moreover, hypertree decompositions require additional data structures and thus
introduce nonnegligible overhead in both runtime and memory consumption. In
this paper, we provide algorithms that exploit hypertree decompositions for the
materialisation and incremental evaluation of Datalog programs. Furthermore, we
combine this approach with standard Datalog reasoning algorithms in a modular
fashion so that the overhead caused by the decompositions is reduced. Our
empirical evaluation shows that, when the program contains complex rules, the
combined approach is usually significantly faster than the baseline approach,
sometimes by orders of magnitude.

本文提供了应用超树分解来优化和简化 Datalog 程序的算法，并与标准 Datalog 和增量规则评估方法相结合。经过实验证明，当程序包含复杂规则时，这种方法通常比基线方法快得多。