Abstract
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.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Aguilera, M.K., Strom, R.E., Sturman, D.C., Astley, M., Chandra, T.D.: Matching events in a content-based subscription system. In: Proceedings of the 18th ACM Symposium on Principles of Distributed Computing, pp. 53–61. ACM Press, New York (1999)
Altinel, M., Franklin, M.J.: Efficient filtering of XML documents for selective dissemination of information. The VLDB Journal, 53–64 (2000)
Campailla, A., Chaki, S., Clarke, E., Jha, S., Veith, H.: Efficient filtering in publish-subscribe systems using binary decision diagrams. In: Proceedings of the 23rd International Conference on Software Engineering, pp. 443–452 (2001)
Fabret, F., Jacobsen, A., Llirbat, F., Pereira, J., Ross, K., Shasha, D.: Filtering algorithms and implementation for very fast publish/subscribe. In: Sellis, T., Mehrotra, S. (eds.) Proceedings of the 20th Intl. Conference on Management of Data (SIGMOD 2001), Santa Barbara, CA, USA, pp. 115–126 (2001)
Carzaniga, A., Rosenblum, D.S., Wolf, A.L.: Design and evaluation of a wide-area event notification service. ACM Transactions on Computer Systems 19, 332–383 (2001)
Mühl, G., Fiege, L., Gärtner, F.C., Buchmann, A.: Evaluating advanced routing algorithms for content-based publish/subscribe systems. In: Proceedings of the 10th IEEE International Symp. on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS 2002), pp. 167–176. IEEE Computer Society, Los Alamitos (2002)
Li, G., Huo, S., Jacobsen, H.: A unified approach to routing, covering and merging in publish/subscribe systems based on modified binary decision diagrams. In: 25th International Conference on Distributed Computing Systems (ICDCS) (2005)
Crespo, A., Buyukkokten, O., Garcia-Molina, H.: Query merging: Improving query subscription processing in a multicast environment. IEEE Transactions on Knowledge and Data Engineering 15 (2003)
Carzaniga, A., Wolf, A.L.: Forwarding in a content-based network. In: Proceedings of ACM SIGCOMM 2003, Karlsruhe, Germany, pp. 163–174 (2003)
Srivastava, D.: Subsumption and indexing in constraint query languages with linear arithmetic constraints. Annals of Mathematics and Artificial Intelligence 8, 315–343 (1992)
Liu, H., Jacobsen, H.A.: Modeling uncertainties in publish/subscribe systems. In: ICDE 2004: Proceedings of the 20th International Conference on Data Engineering, Washington, DC, USA, p. 510. IEEE Computer Society, Los Alamitos (2004)
Chen, Y., Schwan, K.: Opportunistic overlays: Efficient content delivery in mobile ad hoc networks. In: Alonso, G. (ed.) Middleware 2005. LNCS, vol. 3790, pp. 354–374. Springer, Heidelberg (2005)
Dittrich, J.P., Fischer, P.M., Kossmann, D.: Agile: Adaptive indexing for context-aware information filters. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Baltimore, Maryland, USA, June 14-16, pp. 215–226 (2005)
Hauswirth, M., Schmidt, R.: An overlay network for resource discovery in Grids. In: 2nd International Workshop on Grid and Peer-to-Peer Computing Impacts on Large Scale Heterogeneous Distributed Database Systems (GLOBE 2005) (2005)
Ouksel, A.M., Jurca, O., Podnar, I., Aberer, K.: Fast probabilistic subsumption checking for publish/subscribe systems. Technical Report LSIR-REPORT-2006-004, EPFL (2006)
Yan, T.W., García-Molina, H.: Index structures for selective dissemination of information under the Boolean model. ACM Transactions on Database Systems 19, 332–334 (1994)
Voulgaris, S., Riviére, E., Kermarrec, A.M., van Steen, M.: Sub-2-sub: Self-organizing content-based publish and subscribe for dynamic and large scale collaborative networks. In: 5th Int’l Workshop on Peer-to-Peer Systems (IPTPS) (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 IFIP International Federation for Information Processing
About this paper
Cite this paper
Ouksel, A.M., Jurca, O., Podnar, I., Aberer, K. (2006). Efficient Probabilistic Subsumption Checking for Content-Based Publish/Subscribe Systems. In: van Steen, M., Henning, M. (eds) Middleware 2006. Middleware 2006. Lecture Notes in Computer Science, vol 4290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925071_7
Download citation
DOI: https://doi.org/10.1007/11925071_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49023-4
Online ISBN: 978-3-540-68256-1
eBook Packages: Computer ScienceComputer Science (R0)