Efficient probabilistic event stream processing with lineage and Kleene-plus

Zhitao Shen, Hideyuki Kawashima, Hiroyuki Kitagawa

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This paper proposes a working framework and a query language to support probabilistic queries for composite event detection over probabilistic event streams. The language allows users to express Kleene closure patterns for complex event detection in the physical world. Our processing method first detects sequence patterns over probabilistic data streams using AIG, a new data structure, which handles active states with a nondeterministic finite automaton (NFA). Our method then computes the probability of each detected sequence pattern based on its lineage. Through the benefit of lineage, the probability of an output event can be directly calculated without taking into account the query plan. An optimised plan can be selected. Finally, we conducted a performance evaluation of our method and compared the results with the original and optimised query plan. The experiment clearly showed that our proposal outperforms straight-forward query plans.

Original languageEnglish
Pages (from-to)355-374
Number of pages20
JournalInternational Journal of Communication Networks and Distributed Systems
Issue number4
Publication statusPublished - 2009
Externally publishedYes


  • Data stream
  • Event stream processing
  • Kleene-plus
  • Lineage
  • Nondeterministic finite automata
  • Probabilistic data management

ASJC Scopus subject areas

  • Computer Networks and Communications


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