Efficient processing of uncertain events in rule-based systems

Segev Wasserkrug, Avigdor Gal, Opher Etzion, Yulia Turchin

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number5611516
Pages (from-to)45-58
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number1
StatePublished - 2012


  • Complex event processing
  • rule-based reasoning with uncertain information

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Efficient processing of uncertain events in rule-based systems'. Together they form a unique fingerprint.

Cite this