IO3: Interval-Based Out-of-Order Event Processing in Pervasive Computing
In pervasive computing environments, complex event processing has become increasingly important in modern applications. A key aspect of complex event processing is to extract patterns from event streams to make informed decisions in real-time. However, network latencies and machine failures may cause events to arrive out-of-order. In addition, existing literatures assume that events do not have any duration, but events in many real world application have durations, and the relationships among these events are often complex. In this work, we first analyze the preliminaries of time semantics and propose a model of it. A hybrid solution including time-interval to solve out-of-order events is also introduced, which can switch from one level of output correctness to another based on real time. The experimental study demonstrates the effectiveness of our approach.
Keywordspervasive computing complex event out-of-order time interval
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- 1.Pei, J., Han, J., Mortazavi, B., Pinto, H., Chen, Q.: Prefixspan: Mining Sequential Patterns Efficiently by Prefix-projected Pattern Growth. In: Proceedings of the 17th International Conference on Data Engineering (ICDE), pp. 215–226 (2001)Google Scholar
- 3.Wu, E., Diao, Y., Rizvi, S.: High Performance Complex Event Processing over Streams. In: Proceedings of the 32nd SIGMOD International Conference on Management of Data (SIGMOD), pp. 407–418 (2006)Google Scholar
- 4.Mei, Y., Madden, S.: ZStream: a Cost-based Query Processor for Adaptively Detecting Composite Events. In: Proceedings of the 35th SIGMOD International Conference on Management of Data (SIGMOD), pp. 193–206 (2009)Google Scholar
- 6.Liu, M., Li, M., Golovnya, D., Rundenstriner, E.A., Claypool, K.: Sequence Pattern Query Processing over Out-of-Order Event Streams. In: Proceedings of the 25th International Conference on Data Engineering (ICDE), pp. 274–295 (2009)Google Scholar
- 8.Papapetrou, P., Kollios, G., Sclaroff, S., Gunopulos, D.: Discovering Frequent Arrangements of Temporal Intervals. In: Proceedings of the 5th IEEE International Conference on Data Mining, ICDM (2005)Google Scholar
- 10.Patel, D., Hsu, W., Lee, M.L.: Mining Relationships among Interval-based Events for Classification. In: Proceedings of the 34th SIGMOD International Conference on Management of Data (SIGMOD), pp. 393–404 (2008)Google Scholar
- 11.Zhou, C.J., Meng, X.F.: A Framework of Complex Event Detection and Operation in Pervasive Computing. In: The PhD Workshop on Innovative Database Research, IDAR (2009)Google Scholar