Complex Event Recognition Within a Discrete Event System Framework

• 成果类型: Article
• 署名作者: Liu, Yu; Cao, Lin; Shu, Shaolong; Lin, Feng
• 署名单位: Tongji University; Wayne State University; Shanghai Maritime University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3543561
• 发表日期: 2025
• 页码: 4817-4824
• 关键词: automata; semantics; Discrete-event systems; monitoring; Hidden Markov models; ELECTRONIC MAIL; trajectory; training; Sufficient conditions; Soft sensors; complex event recognition; discrete event systems; Pattern recognition

摘要

Recognizing complex events revealed by raw data is an increasingly crucial task that serves as one of the foundations for system monitoring and decision making. Our goal is to accurately recognize the occurred complex events, that is, uniquely determine the occurred complex event sequence from the raw data. We abstract the outputs of data sources as a set of atomic events, and then, use an automaton to describe all atomic event sequences that can be generated by the given system. We represent a complex event as a set of atomic event sequences. For a given atomic event sequence and a complex event to be recognized, we introduce the notion of partition to stand for a possible single complex event sequence. By constructing an augmented automaton that includes all possible partitions, we derive a necessary and sufficient condition for the complex event recognition problem to be solvable. We then find an algorithm to check the condition. When the complex event recognition problem is solvable, any occurred complex event can be determined accurately and promptly online with existing methods like the Aho-Corasick algorithm.