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.

本文提出一种基于模块化框架的材料化计算和维护的方法，该方法使用半朴素算法处理少数规则，同时可使用两种算法计算关系的传递闭包，实验表明，与现有的方法相比，本方法可以处理任意的 datalog 程序，而且性能也要高得多。