Complex event recognition and anomaly detection with event behavior model

The concept of complex event processing refers to the process of tracking and analyzing a set of related events and drawing conclusions from them. For such systems, complex event recognition is essential. The object of complex event recognition is to recognize meaningful events or patterns and construct processing rules to respond to them. Researchers have conducted numerous studies on the recognition of complex event patterns by using recognition languages or models. However, the completeness of the process in complex event recognition has rarely been discussed. Although the reality of the event is uncertain, the structure for modeling and explaining complex event interactions of contingent information remains unclear. In this study, we focused on developing a general framework for addressing these problems and demonstrating the applicability of model-based approaches to represent spatio-temporal dimensions and causality in complex event recognition. In this paper, we propose an event behavior model for complex event recognition from a process perspective. The developed model could detect and explain anomalies associated with complex events. An experiment was conducted to evaluate the model performance. The results revealed that temporal operations within overlapping events were crucial to event pattern recognition.