ABSTRACT
In recent years, there has been a growing need for active systems that can react automatically to events. Some events are generated externally and deliver data across distributed systems, while others are materialized by the active system itself. Event materialization is hampered by uncertainty that may be attributed to unreliable data sources and networks, or the inability to determine with certainty whether an event has actually occurred. Two main obstacles exist when designing a solution to the problem of event materialization with uncertainty. First, event materialization should be performed efficiently, at times under a heavy load of incoming events from various sources. The second challenge involves the generation of a correct probability space, given uncertain events. We present a solution to both problems by introducing an efficient mechanism for event materialization under uncertainty. A model for representing materialized events is presented and two algorithms for correctly specifying the probability space of an event history are given. The first provides an accurate, albeit expensive method based on the construction of a Bayesian network. The second is a Monte Carlo sampling algorithm that heuristically assesses materialized event probabilities. We experimented with both the Bayesian network and the sampling algorithms, showing the latter to be scalable under an increasing rate of explicit event delivery and an increasing number of uncertain rules (while the former is not). Finally, our sampling algorithm accurately and efficiently estimates the probability space.
- Adi, A. A Language and an Execution Model for the Detection of Reactive Situations. Ph.D. Thesis, Technion -- Israel Institute of Technology, 2003.Google Scholar
- Adi, A. and Etzion, O. Amit - the situation manager. The VLDB journal 13, 5 (May 2004), 177--203. Google ScholarDigital Library
- Balazinska, M., Khoussainova, N., and Suciu, D. PEEX: Extracting probabilistic events from rd data. Proceedings of ICDE, (2008).Google Scholar
- Beeri, C., Eyal, A., Milo, T., and Pilberg A. Query-Based Monitoring of BPEL Business Processes. In Proceedings of the ACM-SIGMOD, (2007), 1122--1124. Google ScholarDigital Library
- Breese, J. S., Goldman, R. P., and Wellman, M. P. Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models. IEEE Transactions on Systems, Man and Cybernatics, 24, 11, (1994), 1577.Google Scholar
- Campbell, M., Li, C.-S., Aggarwal, C., Naphade, M., Wu, K.-L., and Zhang, T. An evaluation of over-the-counter medication sales for syndromic surveillance. IEEE International Conference on Data Mining - Life Sciences Data Mining Workshop, (2004).Google Scholar
- Chu, D., Deshpande, A., Hellerstein, J., and Hong, W. Approximate data collection in sensor networks using probabilistic models. In Proceedings ICDE, (2007), page 48. Google ScholarDigital Library
- Cowie, J., Ogielski, A. T., Premore, B., and Yuanb, Y. Internet worms and global routing instabilities. SPIE, 4868, (2002).Google Scholar
- Dayal U. et al. The HiPAC project: Combining active databases and timing constraints. ACM SIGMOD Record, 17, 1, (March 1988), 51--70. Google ScholarDigital Library
- Demers, A., Gehrke, J., Hong, M., Riedewald, M., and White, W. Towards expressive publish/subscribe systems. Advances in Database Technology - EDBT 2006, (2006), 627--644. Google ScholarDigital Library
- Demers, A., Gehrke, J., Panda, B., Riedewald, M., Sharma, V., and White, W. Cayuga: A General Purpose Event Monitoring System. In IDR 2007, Third Biennial Conference on Innovative Data Systems Research, (2007) 412--422.Google Scholar
- Diaz, O., Jaime, A., and Paton, N. Dear: A Debugger for Active Rules in an Object-Oriented Context. In Proceedings of the 1st International Workshop on Rules in Database Systems, (1993) 180--193.Google Scholar
- Forgy, C. L. Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence, 19, (1982), 17--37.Google ScholarDigital Library
- Gehani, N. H., Jagadish, N. H., and Shmueli, O. Composite event specification in active databases: Model and implementation, Proceedings VLDB, (1992), 23--27. Google ScholarDigital Library
- Girod, L., Mei, Y., Newton, R., Rost, S., Thiagarajan, A., Balakrishnan, H., and Madden, S. The Case for a Signal-Oriented Data Stream Management System. In CIDR 2007, (2007) 397--406.Google Scholar
- Green, T. and Tannen, V. Models for incomplete and probabilistic information. IEEE Data Eng. Bull 29, 1 (2006) 17--24.Google Scholar
- Halpern, J. Y. Reasoning about Uncertainty. MIT Press, 2003. Google ScholarDigital Library
- Li, C.-S., Aggarwal, C., Campbell, M., Chang, Y.-C., Glass, G., Iyengar, V., Joshi, M., Lin, C.-Y., Naphade, M., and Smith, J. R. Epi-spire: A system for environmental and public health activity monitoring. IEEE International Conference on Multimedia and Expo, (2003). Google ScholarDigital Library
- Paton, N. W. Active Rules in Database Systems. Springer, 1999. Google ScholarDigital Library
- Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988. Google ScholarDigital Library
- Re, C., Dalvi, N., and Suciu, D. Query evaluation on probabilistic databases. IEEE Data Eng. Bull. 29, 1, (2006) 25--31.Google Scholar
- Shmueli, G. and Fienberg. S. Current and potential statistical methods for monitoring multiple data streams for biosurveillance. In Statistical Methods in Counterterrorism, pages 109--140. Springer Verlag, 2006.Google ScholarCross Ref
- Wasserkrug, S., Gal, A., and Etzion, O. A model for reasoning with uncertain rules in event composition systems. Proceedings of the 21st Annual Conference on Uncertainty in Artificial Intelligence (UAI-05), (2005), 599--606.Google Scholar
- Widom, J. Trio: A system for integrated management of data, accuracy, and lineage. CIDR, (2005), 262--276.Google Scholar
- Widom, J. and Ceri, S., editors. Triggers and Rules for Advanced Database Processing. Morgan-Kaufmann, San Francisco, CA, 1996. Google ScholarDigital Library
- Zimmer, D. and Unland, R. On the Semantics of Complex Events in Active Database Management Systems. Proceedings ICDE (1999), 392--399 Google ScholarDigital Library
Index Terms
- Complex event processing over uncertain data
Recommendations
Introducing uncertainty in complex event processing: model, implementation, and validation
Several application domains involve detecting complex situations and reacting to them. This asks for a Complex Event Processing (CEP) engine specifically designed to timely process low level event notifications to identify higher level composite events ...
Complex event processing over distributed probabilistic event streams
With the rapid development of Internet of Things (IoT), enormous events are produced every day. Complex Event Processing (CEP), which can be used to extract high level patterns from raw data, becomes the key part of the IoT middleware. In large-scale ...
Complex event processing with T-REX
Several application domains involve detecting complex situations and reacting to them. This asks for a Complex Event Processing (CEP) middleware specifically designed to timely process large amounts of event notifications as they flow from the ...
Comments