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Message Correlation and Business Protocol Discovery in Service Interaction Logs

  • Belkacem Serrour
  • Daniel P. Gasparotto
  • Hamamache Kheddouci
  • Boualem Benatallah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5074)

Abstract

The problem of discovering protocols and business processes based on the analysis of log files is a real challenge. The behavior of a Web service can be specified using a Business Protocol, hence the importance of this discovery. The construction of the Business Protocol begins by correlating the logged messages into their conversations (i.e. instances of the business protocol). The accomplishment of this task is easy if we assume that the logs contain the right identifiers, which would allow us to associate every message to a conversation. But in real-world situations, this kind of information rarely exists inside the log files.

Our work consists in correlating the messages present in Web service logs into the conversations they belong to, and then generating automatically the Business Protocol that reflects the messaging behavior perceived in the log. Contrary to other approaches, we do not assume the existence of a conversation identifier. We first model logged message relations using graphs and then we use graph theory techniques to extract the conversations and finally the Business Protocol. Logs are often incomplete and contain errors. This induces some uncertainty on the results. To address this problem, we apply the Dempster-Shafer theory of evidence. Our approach is implemented and tested using synthetic logs.

Keywords

Web services business protocols message correlation log analysis graph theory 

References

  1. 1.
    Benatallah, B., Casati, F., Toumani, F.: Analysis and Management of Web Service Protocols. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, T.-W. (eds.) ER 2004. LNCS, vol. 3288, pp. 524–541. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Benatallah, B., Motahari, H.: Servicemosaic project: Modeling, analysis and management of web services interactions. In: Third Asia-Pacific Conference on Conceptual Modelling (APCCM 2006), vol. 53, pp. 7–9 (2006)Google Scholar
  3. 3.
    Dekar, L., Kheddouci, H.: A cluster based mobility prediction scheme for ad hoc networks. Ad Hoc Networks 6(2), 168–194 (2008)CrossRefGoogle Scholar
  4. 4.
    Devaurs, D., De marchi, F., Hacid, M.S.: Caractérisation des transitions temporisées dans les logs de conversation de services web. In: Extraction et Gestion des Connaissances (EGC 2007), vol. RNTI-E-9, pp. 45–56 (January 2007)Google Scholar
  5. 5.
    Dustdar, S., Gombotz, R.: Discovering web service workflows using web services interaction mining. International Journal of Business Process Integration and Management 1(4), 256–266 (2006)CrossRefGoogle Scholar
  6. 6.
    Greco, G., Guzzo, A., Pontieri, L.: Discovering expressive process models by clustering log traces. IEEE Transactions on Knowledge and Data Engineering 18(8), 1010–1027 (2006)CrossRefGoogle Scholar
  7. 7.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems (2000)Google Scholar
  8. 8.
    Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to automata theory, languages, and computation. In: SIGACT News, 2nd edn., vol. 32(1), pp. 60–65 (March 2001)Google Scholar
  9. 9.
    Motahari, H., Benatallah, B., Saint-Paul, R.: Protocol discovery from imperfect service interaction data. In: Proceedings of the VLDB 2006 Ph.D. Workshop (September 2006)Google Scholar
  10. 10.
    Motahari, H., Saint-Paul, R., Benatallah, B., Casati, F.: Protocol discovery from web service interaction logs. In: ICDE 2007: Proceedings of the IEEE International Conference on Data Engineering (April 2007)Google Scholar
  11. 11.
    Motahari, H., Saint-Paul, R., Benatallah, B., Casati, F., Andritsos, P.: Message correlation for conversation reconstruction in service interaction logs. Technical Report, University of Trento and University of New South Wales (2007)Google Scholar
  12. 12.
    Sentz, K., Ferson, S.: Combination of evidence in dempster-shafer theory. Technical report, Sandia National Laboratories (2002)Google Scholar
  13. 13.
    van der Aalst, W., Weijters, A., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Belkacem Serrour
    • 1
  • Daniel P. Gasparotto
    • 1
  • Hamamache Kheddouci
    • 1
  • Boualem Benatallah
    • 2
  1. 1.Université de Lyon, Laboratoire LIESPVilleurbanne CedexFrance
  2. 2.CSE, UNSWSedneyAustralia

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