Automatic Generation of CEP Rules using Data Analysis Techniques and Model-Driven Engineering

The widespread use of Internet of Things (IoT) systems results in a large volume of data that needs to be analyzed in real time for efficient usage. Complex Event Processing (CEP) is a technique that enables organizations to analyze data and identify complex and meaningful events. CEP uses patterns written in an Event Pattern Language (EPL) to describe an event. However, producing patterns and rules using EPL remains a significant challenge.This research benefits from data analysis and model-driven engineering to automatically produce CEP rules that allow domain experts to extract and define patterns automatically, even if unfamiliar with data analysis or CEP rule design. At the end of the paper, a case problem is solved, resulting in a coverage coefficient of 60%, an average accuracy of 82.5%, and an average precision of 82%. The results show that this combined approach is beneficial in providing a more appropriate response in less time.