Abstract
Event prediction over event streams is an important problem with broad applications. For this problem, rules with predicate events and consequent events are given, and then current events are matched with the predicate events to predict future events. Over the event stream, some matches of predicate events may trigger duplicate predictions, and an effective scheme is proposed to avoid such redundancies. Based on the scheme, we propose a novel approach CBS-Tree to efficiently match the predicate events over event streams. The CBS-Tree approach maintains the recently arrived events as a tree structure, and an efficient algorithm is proposed for the matching of predicate events on the tree structure, which avoids exhaustive scans of the arrived events. By running a series of experiments, we show that our approach is more efficient than the previous work for most cases.
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
Abadi, D.J., et al.: Aurora: A Data Stream Management System. In: Proceedings of the ACM SIGMOD Conference, p. 666 (2003)
Cho, C.W., Zheng, Y., Chen, A.L.P.: Continuously Matching Episode Rules for Predicting Future Events over Event Streams. In: Proceedings of joint conference of Asia-Pacific Web Conference and International Conference on Web-Age Information Management, pp. 884–891 (2007)
Cho, C.W., Zheng, Y., Chen, A.L.P.: CBS-Tree: Event Prediction Using Episode Rules over Event Streams, Tech. Report CS-1207-31, Department of Computer Science, National Tsing Hua University (December 2007)
Demers, A.J., Gehrke, J., Hong, M.S., Riedewald, M., White, W.M.: Towards Expressive Publish/Subscribe Systems. In: Proceedings of International Conference on Extending Database Technology, pp. 627–644 (2006)
Franklin, M.J., Jeffery, S.R., Krishnamurthy, S., Reiss, F., Rizvi, S., Wu, E., Cooper, O., Edakkunni, A., Hong, W.: Design Considerations for High Fan-In Systems: The HiFi Approach. In: Proceedings of Biennial Conference on Innovative Data Systems Research, pp. 290–304 (2005)
Gatziu, S., Dittrich, K.R.: SAMOS: an Active Object-Oriented Database System. IEEE Database Engineering Bulletin 15(1-4), 23–26 (1992)
Gehani, N.H., Jagadish, H.V., Shmueli, O.: Composite Event Specification in Active Databases: Model & Implementation. In: Proceedings of International Conference on Very Large Data Bases, pp. 327–338 (1992)
Hall, F.L.: Traffic stream characteristics, Traffic Flow Theory. U.S. Federal Highway Administration (1996)
Hätönen, K., Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H.: Knowledge Discovery from Telecommunication Network Alarm Databases. In: Proceedings of International Conference on Data Engineering, pp. 112–115 (1996)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery 1(3), 259 (1997)
Ng, A., Fu, A.W.C.: Mining Frequent Episodes for Relating Financial Events and Stock Trends. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 27–39 (2003)
Wang, F., Liu, P.: Temporal Management of RFID Data. In: Proceedings of International Conference on Very Large Data Bases, pp. 1128–1139 (2006)
Wu, E., Diao, Y., Rizvi, S.: High-performance complex event processing over streams. In: Proceedings of the ACM SIGMOD Conference, pp. 407–418 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cho, CW., Zheng, Y., Wu, YH., Chen, A.L.P. (2008). A Tree-Based Approach for Event Prediction Using Episode Rules over Event Streams. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2008. Lecture Notes in Computer Science, vol 5181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85654-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-540-85654-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85653-5
Online ISBN: 978-3-540-85654-2
eBook Packages: Computer ScienceComputer Science (R0)