Sequence Clustering-based Automated Rule Generation for Adaptive Complex Event Processing

In Complex Event Processing (CEP), complex events are detected according to a set of rules that are defined by domain experts. However, it makes the reliability of the system decreased as dynamic changes occur in the domain environment or domain experts make mistakes. To address such problem, this study proposes a Sequence Clustering-based Automated Rule Generation (SCARG) that can automatically generate rules by mining decision-making history of domain experts based on sequence clustering and probabilistic graphical modeling. Furthermore, based on a two-way learning approach, the proposed method is able to support automated regular or occasional rule updates. It makes self-adaptive CEP system possible by combining the rule generation method and the existing dynamic CEP systems. This technique is verified by establishing an automated stock trading system, and the performance of the system is measured in terms of the rate of return. The study solves the aforementioned problems and shows excellent results with an increase of 19.32% in performance when compared to the existing dynamic CEP technique.