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：多项应用需要在高速度符号事件流中及时检测关键情况（如安全与透明度威胁）。这一需求推动了以下两方面发展：（i）事件规范语言——通过时间模式基于简单事件定义复合事件；（ii）流推理框架——评估以这些语言表达的模式。然而，事件规范语言通常被孤立研究，导致其表达能力难以比较，且关联流推理器的适用范围模糊不清。为缓解此问题，我们将主流事件规范语言中的实用片段映射到时序数据日志（Temporal Datalog->-）——一种带有分层否定且无未来依赖的时序数据日志。为支持对时序数据日志的高效流推理，我们提出流式触发图（Streaming Trigger Graphs），该技术扩展了数据日志物化的最新方法。我们的方法构建了统一的复合事件识别机制，具备跨多种实用事件规范语言泛化的潜力。