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A Rule-Based Language for Complex Event Processing and Reasoning

  • Darko Anicic
  • Paul Fodor
  • Sebastian Rudolph
  • Roland Stühmer
  • Nenad Stojanovic
  • Rudi Studer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6333)

Abstract

Complex Event Processing (CEP) is concerned with timely detection of complex events within multiple streams of atomic occurrences. It has useful applications in areas including financial services, mobile and sensor devices, click stream analysis etc. Numerous approaches in CEP have already been proposed in the literature. Event processing systems with a logic-based representation have attracted considerable attention as (among others reasons) they feature formal semantics and offer reasoning service. However logic-based approaches are not optimized for run-time event recognition (as they are mainly query-driven systems). In this paper, we present an expressive logic-based language for specifying and combining complex events. For this language we provide both a syntax as well as a formal declarative semantics. The language enables efficient run time event recognition and supports deductive reasoning. Execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present the performance results showing the competitiveness of our approach.

Keywords

Complex Event Event Processing Atomic Event Event Pattern Event Stream 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Agrawal, J., Diao, Y., Gyllstrom, D., Immerman, N.: Efficient pattern matching over event streams. In: SIGMOD (2008)Google Scholar
  2. 2.
    Alferes, J.J., Banti, F., Brogi, A.: An event-condition-action logic programming language. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds.) JELIA 2006. LNCS (LNAI), vol. 4160, pp. 29–42. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843 (1983)zbMATHCrossRefGoogle Scholar
  4. 4.
    Alves, A.: Extensions to logic programming inference engines to support cep. In: RuleML ’09 (2009)Google Scholar
  5. 5.
    Anicic, D., Fodor, P., Rudolph, S., Stühmer, R., Stojanovic, N., Studer, R.: Etalis: Rule-based reasoning in event processing. In: Helmer, S., Poulovassilis, A., Xhafa, F. (eds.) Reasoning in Event-based Distributed Systems, Studies in Computational Intelligence Series. LNCS. Springer, Heidelberg (2010)Google Scholar
  6. 6.
    Arasu, A., Babu, S., Widom, J.: The cql continuous query language: Semantic foundations and query execution. In: VLDB Journal (2003)Google Scholar
  7. 7.
    Bry, F., Eckert, M.: Rule-based composite event queries: The language xchangeeq and its semantics. In: Marchiori, M., Pan, J.Z., Marie, C.d.S. (eds.) RR 2007. LNCS, vol. 4524, pp. 16–30. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Chakravarthy, S., Mishra, D.: Snoop: an expressive event specification language for active databases. In: Data Knowledge Engineering, Elsevier Science Publishers B. V., Amsterdam (1994)Google Scholar
  9. 9.
    Etzion, O., Niblett, P.: Event Processing in Action. Manning Publications Co., Greenwich (2010)Google Scholar
  10. 10.
    Forgy, C.L.: Rete: A fast algorithm for the many pattern/ many object pattern match problem. Artificial Intelligence (1982)Google Scholar
  11. 11.
    Gatziu, S., Dittrich, K.R.: Samos: an active object-oriented database system. In: IEEE Bulletin of the TC on Data Engineering (1992)Google Scholar
  12. 12.
    Haley, P.: Data-driven backward chaining. In: International Joint Conferences on Artificial Intelligence, Milan, Italy (1987)Google Scholar
  13. 13.
    Kowalski, R., Sergot, M.: A logic-based calculus of events. In: New Generation Computing, Ohmsha (1986)Google Scholar
  14. 14.
    Krämer, J., Seeger, B.: Semantics and implementation of continuous sliding window queries over data streams. ACM Trans. Database Syst. (2009)Google Scholar
  15. 15.
    Lausen, G., Ludäscher, B., May, W.: On active deductive databases: The statelog approach. In: ILPS’97 (1998)Google Scholar
  16. 16.
    Miller, R., Shanahan, M.: The event calculus in classical logic - alternative axiomatisations. Electron. Trans. Artif. Intell. (1999)Google Scholar
  17. 17.
    Paschke, A., Kozlenkov, A., Boley, H.: A homogenous reaction rules language for complex event processing. In: International Workshop on Event Drive Architecture for Complex Event Process. ACM, New York (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Darko Anicic
    • 1
  • Paul Fodor
    • 2
  • Sebastian Rudolph
    • 3
  • Roland Stühmer
    • 1
  • Nenad Stojanovic
    • 1
  • Rudi Studer
    • 1
  1. 1.FZI Research Center for Information TechnologyUniversity of KarlsruheGermany
  2. 2.State University of New York at Stony BrookUSA
  3. 3.Institut AIFBUniversity of KarlsruheKarlsruheGermany

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