“Everything Personal, Not Just Business:” Improving User Experience through Rule-Based Service Customization

  • Richard Hull
  • Bharat Kumar
  • Daniel Lieuwen
  • Peter F. Patel-Schneider
  • Arnaud Sahuguet
  • Sriram Varadarajan
  • Avinash Vyas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2910)


The web and converged services paradigm promises tremendous flexibility in the creation of rich composite services for enterprises and end-users. The flexibility and richness offers the possibility of highly customized, individualized services for the end user and hence revenue generating services for service providers (e.g., ASPs, telecom network operators, ISPs). But how can end-users (and enterprises) specify their preferences when a myriad of possibilities and potential circumstances need to be addressed? In this paper we advocate a solution based on policy management where user preferences are specified through forms but translated into rules in a high-level policy language. This paper identifies the requirements for this kind of interpretation, and describes the Houdini system (under development at Bell Labs) which offers a rich rule-based language and a framework that supports intuitive, forms-based provisioning interfaces.


Service Selection Composite Service Policy Decision Point Rule Engine Policy Enforcement Point 
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.


  1. 1.
    3GPP. Generic User Profile (2001),
  2. 2.
    Adomavicius, G., Tuzhilin, A.: User profiling in personalization applications through rule discovery and validation. In: Proc. Fifth ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (1999)Google Scholar
  3. 3.
    Open Mobile Alliance,
  4. 4.
    Ao, X., Minsky, N., Nguyen, T.D.: A hierarchical policy specification language, and enforcement mechanism, for governing digitual enterprises. In: Proc. of IEEE 3rd Intl.Workshop on Policies for Distributed Systems and Networks, Policy2002 (2002)Google Scholar
  5. 5.
    Brownston, L., Farrell, R., Kant, E., Martin, N.: Programming Expert Systems in OPS5: An Introduction to Rule-Based Programming. Addison-Wesley, Reading (1985)Google Scholar
  6. 6.
    Christophides, V., Hull, R., Kumar, A.: Querying and splicing of XML workflows. In: Batini, C., Giunchiglia, F., Giorgini, P., Mecella, M. (eds.) CoopIS 2001. LNCS, vol. 2172, p. 386. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Clifton, C., Fundulaki, I., Hull, R., Kumar, B., Lieuwen, D., Sahuguet, A.: Privacy-enhanced data management for next-generation e-commerce. In: Proc. VLDB (2003) (to appear)Google Scholar
  8. 8.
    Forgy, C.L.: Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem. Artificial Intelligence 19, 17–37 (1982)CrossRefGoogle Scholar
  9. 9.
    PAM Forum. Presence and availability forum home page,
  10. 10.
    Apache Foundation. Module mod_rewrite URL Rewriting Engine,
  11. 11.
    Parlay Group. The Parlay Group – specifications,
  12. 12.
    Hull, R., Benedikt, M., Christophides, V., Su, J.: E-services: A look behind the curtain. In: Proc. ACM Symp. on Principles of Database Systems (PODS), pp. 1–14 (2003)Google Scholar
  13. 13.
    Hull, R., Kumar, B., Lieuwen, D.: Towards federated policy management. In: Proc. IEEE Policy 2003 (2003)Google Scholar
  14. 14.
    Hull, R., Kumar, B., Lieuwen, D., Patel-Schneider, P., Sahuguet, A., Varadarajan, S., Vyas, A.: A policy-based system for personalized and privacy-conscious user data sharing. Technical report, Bell Labs (2003),
  15. 15.
    Hull, R., Kumar, B., Lieuwen, D., Patel-Schneider, P., Sahuguet, A., Varadarajan, S., Vyas, A.: Enabling context-aware and privacy-conscious user data sharing. In: Proc. IEEE Intl. Conf. on Mobile Data Management (2004) (to appear)Google Scholar
  16. 16.
    Hull, R., Llirbat, F., Kumar, B., Zhou, G., Dong, G., Su, J.: Optimization techniques for data-intensive decision flows. In: Proc. IEEE Intl. Conf. on Data Engineering (2000)Google Scholar
  17. 17.
  18. 18.
    ILOG. ILOG Rules,
  19. 19.
    iMerge Enhanced Business Services (EBS),
  20. 20.
  21. 21.
    Mitchell, T.: Decision tree learning. In: Mitchell, T. (ed.) Machine Learning, pp. 52–78. McGraw-Hill, New York (1997)Google Scholar
  22. 22.
  23. 23.
    OASIS. XML Access Control Language,
  24. 24.
    Pearlman, L., Foster, I., Welch, V., Kesselman, C., Tuecke, S.: A community authorization service for group collaboration. In: Proc. of IEEE 3rd Intl.Workshop on Policies for Distributed Systems and Networks, Policy2002 (2002)Google Scholar
  25. 25.
    Sahuguet, A., Hull, R., Lieuwen, D., Xiong, M.: Enter Once, Share Everywhere: User Profile Management in Converged Networks. In: Proc. Conf. on Innovative Database Research (CIDR) (January 2003)Google Scholar
  26. 26.
    Appium Technologies. Fuzion-UC: Unified communications, October 1 (2002),
  27. 27.
    W3C. A P3P Preference Exchange Language (APPEL),
  28. 28.
  29. 29.
    W3C. Platform for Internet Content Selection (PICS),
  30. 30.
    W3C. Platform for Privacy Preferences Project (P3P),

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Richard Hull
    • 1
  • Bharat Kumar
    • 1
  • Daniel Lieuwen
    • 1
  • Peter F. Patel-Schneider
    • 1
  • Arnaud Sahuguet
    • 1
  • Sriram Varadarajan
    • 1
  • Avinash Vyas
    • 1
  1. 1.Bell Labs, Lucent TechnologiesMurray HillUSA

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