ABSTRACT
Detection of stateful complex event patterns using parallel programming features is a challenging task because of statefulness of event detection operators. Parallelization of event detection tasks needs to be implemented in a way that keeps track of state changes by new arriving events.
In this paper, we describe our implementation for a customized complex event detection engine by using Open Multi-Processing (OpenMP), a shared memory programming model. In our system event detection is implemented using Deterministic Finite Automata (DFAs). We implemented a data stream aggregator that merges 4 given event streams into a sequence of C++ objects in a buffer used as source event stream for event detection in a next processing step. We describe implementation details and 3 architectural variations for stream aggregation and parallelized of event processing. We conducted performance experiments with each of the variations and report some of our experimental results. A comparison of our performance results shows that for event processing on single machine with multi cores and limited memory, using mutli-threads with shared buffer has better stream processing performance than an implementation with multi-processes and shared memory.
- S. Fathollahzadeh, R. Karimi, M. Sharifi, K. Teymourian, A. Hasan, and A. Paschke. Parallel event processing on unbound streams with multi-step windowing. In Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, DEBS '15, pages 328--329, New York, NY, USA, 2015. ACM. Google ScholarDigital Library
- K. Teymourian and A. Paschke. Plan-based semantic enrichment of event streams. In ESWC, volume 8465 of Lecture Notes in Computer Science, pages 21--35. Springer, 2014.Google Scholar
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- S. V. Vincenzo Gulisano, Zbigniew Jerzak and H. Ziekow. ACM International Conference on Distributed Event-Based Systems (DEBS), 2016. http://www.debs2016.org/.Google Scholar
Index Terms
Stateful complex event detection on event streams using parallelization of event stream aggregations and detection tasks
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