Automatic Description of Context-Altering Services through Observational Learning

  • Katharina Rasch
  • Fei Li
  • Sanjin Sehic
  • Rassul Ayani
  • Schahram Dustdar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7319)


Understanding the effect of pervasive services on user context is critical to many context-aware applications. Detailed descriptions of context-altering services are necessary, and manually adapting them to the local environment is a tedious and error-prone process. We present a method for automatically providing service descriptions by observing and learning from the behavior of a service with respect to its environment. By applying machine learning techniques on the observed behavior, our algorithms produce high quality localized service descriptions. In a series of experiments we show that our approach, which can be easily plugged into existing architectures, facilitates context-awareness without the need for manually added service descriptions.


Smart Home Context Change Service Description Capability Learning Service Execution 
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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  1. 1.
    KNX standard (Version 1.1). Konnex Association Brussels (2004)Google Scholar
  2. 2.
    Bowers, S., Ludäscher, B.: Towards Automatic Generation of Semantic Types in Scientific Workflows. In: Dean, M., Guo, Y., Jun, W., Kaschek, R., Krishnaswamy, S., Pan, Z., Sheng, Q.Z. (eds.) WISE 2005 Workshops. LNCS, vol. 3807, pp. 207–216. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Carman, M.J., Knoblock, C.A.: Learning semantic descriptions of web information sources. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 2695–2700. Morgan Kaufmann Publishers Inc., San Francisco (2007)Google Scholar
  4. 4.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41, 15:1–15:58 (2009)Google Scholar
  5. 5.
    Ciccio, C.D., Mecella, M., Caruso, M., Forte, V., Iacomussi, E., Rasch, K., Querzoni, L., Santucci, G., Tino, G.: The homes of tomorrow: service composition and advanced user interfaces. ICST Transactions on Ambient Systems 11(10-12) (2011)Google Scholar
  6. 6.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)Google Scholar
  7. 7.
    Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 144–151. Morgan Kaufmann Publishers Inc., San Francisco (1998)Google Scholar
  8. 8.
    Heß, A., Johnston, E., Kushmerick, N.: ASSAM: A Tool for Semi-automatically Annotating Semantic Web Services. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 320–334. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    ISO 29341-1:2008: Part 1: UPnP Device Architecture Version 1.0. International Organization for Standardization, Geneva, SwitzerlandGoogle Scholar
  10. 10.
    Kaldeli, E., Warriach, E.U., Bresser, J., Lazovik, A., Aiello, M.: Interoperation, composition and simulation of services at home. In: Eigth International Conference on Service Oriented Computing, pp. 167–181 (2010)Google Scholar
  11. 11.
    Khalid, B., Embury, S.M., Paton, N.W., Stevens, R., Goble, C.A.: Automatic annotation of web services based on workflow definitions. ACM Trans. Web 2, 11:1–11:34 (2008)Google Scholar
  12. 12.
    Lerman, K., Plangprasopchok, A., Knoblock, C.A.: Automatically labeling the inputs and outputs of web services. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 2, pp. 1363–1368. AAAI Press (2006)Google Scholar
  13. 13.
    Li, F., Rasch, K., Truong, H.L., Ayani, R., Dustdar, S.: Proactive service discovery in pervasive environments. In: Proceedings of the 7th International Conference on Pervasive Services, pp. 126–133 (2010)Google Scholar
  14. 14.
    Li, F., Sehic, S., Dustdar, S.: Copal: An adaptive approach to context provisioning. In: 2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 286–293 (2010)Google Scholar
  15. 15.
    Patil, A.A., Oundhakar, S.A., Sheth, A.P., Verma, K.: METEOR-S web service annotation framework. In: Proceedings of the 13th International Conference on World Wide Web, WWW 2004, pp. 553–562. ACM, New York (2004)CrossRefGoogle Scholar
  16. 16.
    Rasch, K., Li, F., Sehic, S., Ayani, R., Dustdar, S.: Context-driven personalized service discovery in pervasive environments. World Wide Web 14(4), 295–319 (2011)CrossRefGoogle Scholar
  17. 17.
    Wu, D., Parsia, B., Sirin, E., Hendler, J., Nau, D.: Automating DAML-S Web Services Composition Using SHOP2. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 195–210. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Katharina Rasch
    • 1
  • Fei Li
    • 2
  • Sanjin Sehic
    • 2
  • Rassul Ayani
    • 1
  • Schahram Dustdar
    • 2
  1. 1.School of Information and Communication TechnologyKTH Royal Institute of TechnologyStockholmSweden
  2. 2.Distributed Systems GroupVienna University of TechnologyWienAustria

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