Efficient Probabilistic Subsumption Checking for Content-Based Publish/Subscribe Systems

  • Aris M. Ouksel
  • Oana Jurca
  • Ivana Podnar
  • Karl Aberer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4290)


Efficient subsumption checking, deciding whether a subscription or publication is covered by a set of previously defined subscriptions, is of paramount importance for publish/subscribe systems. It provides the core system functionality—matching of publications to subscriber needs expressed as sub-scriptions—and additionally, reduces the overall system load and generated traffic since the covered subscriptions are not propagated in distributed environments. As the subsumption problem was shown previously to be co-NP complete and existing solutions typically apply pairwise comparisons to detect the subsumption relationship, we propose a ‘Monte Carlo type’ probabilistic algorithm for the general subsumption problem. It determines whether a publication/subscription is covered by a disjunction of subscriptions in O(k m d), where k is the number of subscriptions, m is the number of distinct attributes in subscriptions, and d is the number of tests performed to answer a subsumption question. The probability of error is problem-specific and typically very small, and sets an upper bound on d. Our experimental results show significant gains in term of subscription set reduction which has favorable impact on the overall system performance as it reduces the total computational costs and networking traffic. Furthermore, the expected theoretical bounds underestimate algorithm performance because it performs much better in practice due to introduced optimizations, and is adequate for fast forwarding of subscriptions in case of high subscription rate.


Group Coverage Binary Decision Diagram Probabilistic Algorithm Active Subscription Fast Forwarding 
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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Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Aris M. Ouksel
    • 1
  • Oana Jurca
    • 2
  • Ivana Podnar
    • 2
  • Karl Aberer
    • 2
  1. 1.Depts. Of Information and Decision Sciences and Computer ScienceThe University of Illinois at ChicagoUSA
  2. 2.School of Computer and Communication SciencesEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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