CEP Rule Extraction Framework Based on Evolutionary Algorithm

Complex Event Processing (CEP) is an effective method to find the time and causality relationship between various events in the stream data. Its purpose is to match the low-level events in the event stream into complex events according to a certain pattern. CEP has a wide range of applications in the Internet of Things, cloud computing, finance and cyber security. Currently, in CEP design, event matching rules are mainly formulated by domain experts according to their professional knowledge and subjective judgment. However, with the increase of the complexity of event flow data, it is increasingly difficult to formulate rules. To solve this problem, a CEP rule extraction framework based on an evolutionary algorithm is proposed in this study to realize automatic learning of CEP rules, and test data are used for verification, and high-precision experimental results are obtained.