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Stateful complex event detection on event streams using parallelization of event stream aggregations and detection tasks

Published:13 June 2016Publication History

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.

References

  1. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  2. 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 ScholarGoogle Scholar
  3. K. Teymourian, M. Rohde, and A. Paschke. Fusion of background knowledge and streams of events. In DEBS, pages 302--313. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. V. Vincenzo Gulisano, Zbigniew Jerzak and H. Ziekow. ACM International Conference on Distributed Event-Based Systems (DEBS), 2016. http://www.debs2016.org/.Google ScholarGoogle Scholar

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  1. Stateful complex event detection on event streams using parallelization of event stream aggregations and detection tasks

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    • Published in

      cover image ACM Conferences
      DEBS '16: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems
      June 2016
      456 pages
      ISBN:9781450340212
      DOI:10.1145/2933267

      Copyright © 2016 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 June 2016

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      Overall Acceptance Rate130of553submissions,24%
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