  Efficient matching of incoming events of data streams to persistent queries
is fundamental to event stream processing systems. These applications require
dealing with high volume and continuous data streams with fast processing time
on distributed complex event processing (CEP) systems. Therefore, a
well-managed parallel processing technique is needed for improving the
performance of the system. However, the specific properties of pattern
operators in the CEP systems increase the difficulties of the parallel
processing problem. To address these issues, a parallelization model and an
adaptive parallel processing strategy are proposed for the complex event
processing by introducing a histogram, and utilizing the probability and queue
theory. The proposed strategy can estimate the optimal event splitting policy,
which can suit the most recent workloads conditions such that the selected
policy has the least expected waiting time for further processing the arriving
events. The proposed strategy can keep the CEP system running fast under the
variation of the time window sizes of operators and input rates of streams.
Finally, the utility of our work is demonstrated through the experiments on the
StreamBase system.
