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 procedure which, analogously to the classical seminaive algorithm in Datalog, aims at minimising redundant computation by ensuring that each temporal rule instance is considered at most once during the execution of the algorithm. Our experiments show that our optimised seminaive strategy for DatalogMTL is able to significantly reduce materialisation times.

翻译：DatalogMTL 是数据日志的延伸, 与基于时间时间的操作者一起发现在基于时间的内科数据访问和查询回答以及数据流推理方面的应用。 数据日志的实用算法依赖于基于材料化的推理, 时间性事实在连续几轮规则应用中以远端链式的方式产生。 然而, 目前的基于材料化程序是基于天真的评价战略, 造成效率低下的主要来源来自重复计算。 本文中我们建议采用基于材料化的程序, 类似于数据日志的经典半成式算法, 目的是通过确保每个时间规则实例在算法执行期间最多一次得到考虑, 从而将多余的计算最小化。 我们的实验表明, 我们的数据日志的优化半成半成战略能够大大缩短材料化时间。