高效的时间Datalog物化用于复合事件识别

Seminaive Materialisation in DatalogMTL

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 proced…

Modular Materialisation of Datalog Programs

The seminaïve 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 rule…

Optimised Maintenance of Datalog Materialisations

To efficiently answer queries, datalog systems often materialise all consequences of a datalog program, so the materialisation must be updated whenever the input facts change. Several solutions to the materialisation update problem have been proposed. The Delete/Rederive (DRed) and the Backward/Forward (B/F) algorithms solve this problem for general datalog, but both contain steps that evaluate rules 'backwards' by matching their heads to a fact and evaluating the partially instantiated rule bodies as queries. We show that this can be a considerable source of overhead even on very small update…

增量维护DatalogMTL材料化

Incremental Maintenance of DatalogMTL Materialisations

本文提出DRedMTL算法，用于高效维护DatalogMTL的增量材料化，通过周期性区间处理提升动态更新效率。

Datalog Reasoning over Compressed RDF Knowledge Bases

Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all possible rule applications and can use a lot of memory for storing the derived facts, which can hinder performance. We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing. Our technique can thus require less space, as well as skip certain rule applications. Our …

面向Datalog推理的优化存储

Optimised Storage for Datalog Reasoning

针对Datalog推理中物化事实存储开销大的问题，提出可集成优化存储方案与标准物化算法的通用框架，并为传递、并集规则设计专用存储方案，大幅降低内存消耗且查询性能具竞争力。