Join Query Optimization Techniques for Complex Event Processing Applications

Complex event processing (CEP) is a prominent technology used in many modern applications for monitoring and tracking events of interest in massive data streams. CEP engines inspect real-time information flows and attempt to detect combinations of occurrences matching predefined patterns. This is done by combining basic data items, also called primitive events, according to a pattern detection plan, in a manner similar to the execution of multi-join queries in traditional data management systems. Despite this similarity, little work has been done on utilizing existing join optimization methods to improve the performance of CEP-based systems. In this paper, we provide the first theoretical and experimental study of the relationship between these two research areas. We formally prove that the CEP Plan Generation problem is equivalent to the Join Query Plan Generation problem for a restricted class of patterns and can be reduced to it for a considerably wider range of classes. This result implies the NP-completeness of the CEP Plan Generation problem. We further show how join query optimization techniques developed over the last decades can be adapted and utilized to provide practically efficient solutions for complex event detection. Our experiments demonstrate the superiority of these techniques over existing strategies for CEP optimization in terms of throughput, latency, and memory consumption.