Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Babu, S., et al.: Exploiting K-constraints to Reduce Memory Overhead in Continuous Queries over Data Streams. ACM Transaction on Database Systems 29(3), 545–580 (2004)
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)
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)
Alex, D., Robert, R., Subrahmanian, V.S.: Probabilistic Temporal Databases. ACM Transaction on Database Systems 26(1), 41–95 (2001)
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)
Kam, P.S., Fu, A.W.: Discovering Temporal Patterns for Interval-based Events. In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds.) DaWaK 2000. LNCS, vol. 1874, pp. 317–326. Springer, Heidelberg (2000)
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)
Wu, S., Chen, Y.: Mining Nonambiguous Temporal Patterns for Interval-based Events. IEEE Transactions on Knowledge and Data Engineering 19(6), 742–758 (2007)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, C., Meng, X. (2010). IO3: Interval-Based Out-of-Order Event Processing in Pervasive Computing. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12098-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-12098-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12097-8
Online ISBN: 978-3-642-12098-5
eBook Packages: Computer ScienceComputer Science (R0)