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Issue No. 01 - January (2012 vol. 24)
ISSN: 1041-4347
pp: 45-58
Segev Wasserkrug , IBM Haifa Research Lab, Haifa
Avigdor Gal , Technion - Israel Institute of Technology, Haifa
Opher Etzion , IBM Haifa Research Lab, Haifa
Yulia Turchin , Technion - Israel Institute of Technology, Haifa
There is a growing need for systems that react automatically to events. While some events are generated externally and deliver data across distributed systems, others need to be derived by the system itself based on available information. Event derivation is hampered by uncertainty attributed to causes such as unreliable data sources or the inability to determine with certainty whether an event has actually occurred, given available information. Two main challenges exist when designing a solution for event derivation under uncertainty. First, event derivation should scale under heavy loads of incoming events. Second, the associated probabilities must be correctly captured and represented. We present a solution to both problems by introducing a novel generic and formal mechanism and framework for managing event derivation under uncertainty. We also provide empirical evidence demonstrating the scalability and accuracy of our approach.
Complex event processing, rule-based reasoning with uncertain information.

O. Etzion, S. Wasserkrug, Y. Turchin and A. Gal, "Efficient Processing of Uncertain Events in Rule-Based Systems," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 45-58, 2010.
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