A rule modeling engine for complex event processing (a case study on passive RFID for a virtual shopping mall)

Abstract

Optimizing Complex Event Processing (CEP) patterns become more interesting topic for researchers due to highly demanding in different areas including RFID based inventory management, Decision support systems, intrusion detection in networks, and many other systems dealing with pattern matching over time series data. Regular expression matching is a well-studied field. In order to achieve better results, one solution is to revise existing algorithms and techniques to make patterns shorter and reducing system overload. In this study, we proposed a complex event processing engine considering historical data in the process of generating more efficient pattern for incoming events. An algorithm is proposed to act on events based on the engine. We develop a pattern matching unit which is used to find match cases over arriving events. Experimental results have shown promising outcomes in reducing processing time with multiple patterns.