Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events. This demand has motivated the development of (i) event specification languages, which define composite events via temporal patterns over simpler events, and (ii) stream reasoning frameworks, evaluating patterns expressed in these languages. However, event specification languages are typically studied in isolation, complicating their comparison in terms of expressivity and obscuring the scope of their associated stream reasoners. To mitigate this issue, we map practical fragments of prominent event specification languages into Temporal Datalog->-, a temporal Datalog with stratified negation and no future dependencies. To support efficient stream reasoning over Temporal Datalog->-, we propose Streaming Trigger Graphs, an extension of a state-of-the-art technique for Datalog materialisation. Our approach yields a uniform composite event recognition mechanism that has the potential to generalise across a wide range of practical event specification languages.

翻译：众多应用需要在高速符号事件流中及时检测关键态势（如安全威胁和透明度问题）。这一需求催生了（i）事件规范语言（通过时态模式定义基础事件的复合事件模式）与（ii）流推理框架（评估该类语言表达的时态模式）的发展。然而，事件规范语言通常被孤立研究，这不仅使它们在表达能力上的比较复杂化，也模糊了相关流推理器的适用范围。为解决此问题，我们将主流事件规范语言的实用片段映射到时态Datalog->-（一种具有分层否定且无未来依赖的时态Datalog）。为支持时态Datalog->-的高效流推理，我们提出流式触发图——一种对Datalog物化先进技术的扩展。该方法构建了统一的复合事件识别机制，具有跨广泛实用事件规范语言泛化的潜力。