Long-Term Event Processing over Data Streams in Cyber-Physical Systems

Abstract

Event processing is a crucial cornerstone supporting the revolution of Internet of Things (IoT) and Cyber-Physical Systems (CPS) by integrating physical-layer networking and providing intelligent computation and real-time control abilities. In various IoT and CPS application scenarios, the event processing systems are required to detect complex event patterns using large time window, namely long-term events. The detection of long-term event usually leads to a large number of redundant runtime instances and calculations that significantly deteriorates the system efficiency. In this article, we propose an efficient long-term event processing model, named Long-Term Complex Event Processing (LTCEP). It leverages the semantic constraints calculus to split long-term event into sub-models. We establish a long-term query and intermediate result buffering mechanism to optimize the real-time response ability and throughput performance. Experimental results show that LTCEP can effectively reduce more than 50% redundant runtime states, which provides over 60% faster response performance and around 30% higher system throughput comparing to other selected benchmarks. The results also imply that LTCEP model has better stability and scalability in large-scale event processing applications.