Enhancing performance and expressibility of complex event processing using binary tree-based directed graph

In various domains, applications are required to detect and react to complex situations accordingly. In response to the demand for matching receiving events to complex patterns, several event processing systems have been developed. However, there are just a few of them considered both performance and expressibility of event matching as focusing only on performance can cause negative effect on the expressibility or vice versa. This research develops a fast adaptive event matching system (FAEM), a new event matching system to improve expressibility and performance measures (throughput and end-to-end latency). This system is designed and developed based on a novel binary tree-based directed graph (BTDG) as a unified basis for event-matching. The proposed system transforms a user-defined query into a set of system objects including buffers, conditions on buffers, cursors, and join operators (non-kleene and kleene operators) and arranges these objects on a BTDG. Provided BTDG the enhancement in performance of non-kleene operators applied through developing a batch removal method to remove the events that are located out of time-window, and an actual time window (ATW) which can improve performance of event matching. To improve performance of kleene operators, this research introduces a twin algorithms for kleene operator which is match to BTDG. These two kleene algorithms apply grouping on events and reduce the number of intermediate results and apply combination algorithm in final stage. Transformation of queries containing join operators into BTDG enhances the expressibility of the proposed CEP system

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

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