Analysis of Complex Event Processing with Esper

Most organizations need to make informed decisions quickly to provide competitive advantage. One approach to assist in this need is near real time business intelligence. The field of near real time business analytics has seen advancements over the recent years, not the least of which is the advancement of complex event processing techniques and tools. Complex event processing (CEP) involves gathering and evaluating multiple event streams of data in order to identify complex events. CEP systems can analyze multiple disparate sources of stream data to infer events or patterns that suggest some meaning to the end user. The CEP Blog defines complex event processing as “an emerging network technology that creates actionable, situational knowledge from distributed message-based systems, databases and applications in real time or near real time.”
The applications of CEP systems are widespread: Business Processing Management and Automation, Algorithmic Trading, fraud detection, intrusion detection, SLA Monitoring, air traffic, etc. The emergence of inexpensive utility compute nodes running big data technologies like Hadoop, Cassandra, etc. increase the effectiveness of CEP systems, enabling them to analyze terabytes of input streams faster than previously believed possible. CEP systems offer two major components, a high level language to enable the administrator to easily describe the events, and an infrastructure engine for processing the data streams.
The research will provide a definition of CEP systems, and types of use cases they solve with near real time business intelligence. In addition, a CEP experiment leveraging a popular open source CEP tool, Esper, and various disparate data streams from personal financial tools will be conducted. The goal of this research will be to provide the reader with an understanding of the capabilities of CEP and its practical applications.