In the wake of the recent resurgence of the Datalog language of databases, together with its extensions for ontological reasoning settings, this work aims to bridge the gap between the theoretical studies of DatalogMTL (Datalog extended with metric temporal logic) and the development of production-ready reasoning systems. In particular, we lay out the functional and architectural desiderata of a modern reasoner and propose our system, Temporal Vadalog. Leveraging the vast amount of experience from the database community, we go beyond the typical chase-based implementations of reasoners, and propose a set of novel techniques and a system that adopts a modern data pipeline architecture. We discuss crucial architectural choices, such as how to guarantee termination when infinitely many time intervals are possibly generated, how to merge intervals, and how to sustain a limited memory footprint. We discuss advanced features of the system, such as the support for time series, and present an extensive experimental evaluation. This paper is a substantially extended version of "The Temporal Vadalog System" as presented at RuleML+RR '22. Under consideration in Theory and Practice of Logic Programming (TPLP).

翻译：随着数据库Datalog语言及其面向本体推理场景的扩展近期再度兴起，本研究致力于弥合DatalogMTL（融合度量时序逻辑的Datalog扩展）理论研究与生产级推理系统开发之间的鸿沟。我们系统阐述了现代推理机的功能与架构需求，并提出了我们的系统——时序Vadalog。通过借鉴数据库领域的丰富经验，我们超越了传统的基于追索（chase）的推理机实现方案，提出了一系列创新技术及采用现代数据流水线架构的系统。本文深入探讨了关键架构设计决策，包括：在可能生成无限时间区间时如何保证终止性、如何进行区间合并以及如何维持有限的内存占用。同时，我们论述了系统的高级特性（如时间序列支持），并呈现了广泛的实验评估。本文系RuleML+RR '22会议论文《时序Vadalog系统》的实质性扩展版本，现由《逻辑编程理论与实践》（TPLP）期刊审议中。