Probabilistic Complex Event Recognition: A Survey.
ACM Comput. Surv.(2017)
摘要
Complex event recognition (CER) applications exhibit various types of uncertainty, ranging from incomplete and erroneous data streams to imperfect complex event patterns. We review CER 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. Several 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.
更多查看译文
关键词
Event processing, probabilistic Petri nets, probabilistic automata, probabilistic graphical models, probabilistic logics, stochastic grammars, uncertainty
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络