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Efficient matching of incoming events of data streams to persistent queriesis fundamental to event stream processing systems. These applications requiredealing with high volume and continuous data streams with fast processing timeon distributed complex event processing (CEP) systems. Therefore, awell-managed parallel processing technique is needed for improving theperformance of the system. However, the specific properties of patternoperators in the CEP systems increase the difficulties of the parallelprocessing problem. To address these issues, a parallelization model and anadaptive parallel processing strategy are proposed for the complex eventprocessing by introducing a histogram, and utilizing the probability and queuetheory. The proposed strategy can estimate the optimal event splitting policy,which can suit the most recent workloads conditions such that the selectedpolicy has the least expected waiting time for further processing the arrivingevents. The proposed strategy can keep the CEP system running fast under thevariation of the time window sizes of operators and input rates of streams.Finally, the utility of our work is demonstrated through the experiments on theStreamBase system.

An adaptive parallel processing strategy in complex event processing systems over data streams