概率复杂事件识别研究综述
Probabilistic Complex Event Recognition: A Survey
摘要：
复杂事件识别应用程序表现出各种类型的不确定性，从不完整和错误的数据流到不完美的复杂事件模式。我们回顾复杂事件识别技术，处理，在某种程度上，不确定性。我们研究基于自动机，概率图形模型和一阶逻辑，这是最常见的技术，和基于Petri网和语法，这是不经常使用的方法。确定了一些限制相对于所采用的语言，它们的概率模型和它们的性能，相比，纯粹的确定性的情况下。基于这些局限性，我们强调了未来工作的前景。
英文摘要：
Complex Event Recognition applications exhibit various types of uncertainty, ranging from incomplete and erroneous data streams to imperfect complex event patterns. We review Complex Event Recognition techniques that handle, to some extent, uncertainty. We examine techniques based on automata, probabilistic graphical models and first-order logic, which are the most common ones, and approaches based on Petri Nets and Grammars, which are less frequently used. A number of limitations are identified with respect to the employed languages, their probabilistic models and their performance, as compared to the purely deterministic cases. Based on those limitations, we highlight promising directions for future work.
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