统一多种事件语言，实现高效实时复合事件检测
Efficient Temporal Datalog Materialisation for Composite Event Recognition
- 将主流事件语言映射到带分层否定的时序Datalog
- 提出流触发图技术，支持高效增量推理
- 适用于安全监控、透明性检测等实时场景
许多应用需要在高速符号事件流中及时检测关键情况，如安全威胁和透明性问题。这推动了（i）事件规范语言的发展，通过时间模式定义复合事件；以及（ii）流推理框架，用于评估这些语言表达的模式。然而，现有事件语言常被孤立研究，难以比较其表达能力，也模糊了对应推理系统的作用范围。为此，我们把主流事件语言的实用片段映射到带分层否定且无未来依赖的时序Datalog->-。为支持该形式的高效流推理，我们提出流触发图（Streaming Trigger Graphs），是对当前先进Datalog材料化技术的扩展。该方法提供统一的复合事件识别机制，具备跨多种实际事件语言推广的潜力。
原文摘要 · Abstract (English)
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.
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