FACE – A Knowledge-Intensive Case-Based Architecture for Context-Aware Services

  • Monica Vladoiu
  • Jörg Cassens
  • Zoran Constantinescu
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


Technological progress has made it possible to interact with computer systems and applications anywhere and any time. It is crucial that these applications are able to adapt to the user, as a person, and to its current situation, whatever that is. Contextual information and a mechanism to reason about it have demonstrated an important potential to provide solutions in this respect. This paper aims at providing an integrated CBR architecture to be used in context-aware systems. It is the result of our work to develop ePH, a system for building dynamic user communities that share public interest information and knowledge that is accessible through always-on, context-aware services.


knowledge-intensive case-based reasoning context-aware services user modeling context modeling knowledge base 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kofod-Petersen, A., Mikalsen, M.: Context: Representation and Reasoning. Representing and Reasoning about Context in a Mobile Environment. Revue d’Intelligence Artificielle 19(3), 479–498 (2005)CrossRefGoogle Scholar
  2. 2.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7(1), 39–59 (1994)Google Scholar
  3. 3.
    Anderson, J.R.: The Architecture of Cognition. Harvard University Press, Cambridge (1983)Google Scholar
  4. 4.
    Schank, R.: Dynamic memory; a theory of reminding and learning in computers and people. Cambridge University Press, Cambridge (1982)Google Scholar
  5. 5.
    Lee, J.S., Lee, J.C.: Context Awareness by CBR in a Music Recommendation System. In: Ichikawa, H., Cho, W.-D., Satoh, I., Youn, H.Y. (eds.) UCS 2007. LNCS, vol. 4836, pp. 45–58. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Kofod-Petersen, A.: Challenges in CBR for Context Awareness in Ambient Intelligent Systems. In: Int’l Workshop on CBR and Context Awareness, CACOA 2006 (2006)Google Scholar
  7. 7.
    Corchado, J.M., Bajo, J., de Paz, Y.: A CBR System: The Core of an Ambient Intelligence Health Care Application. In: Soft Computing Applications in Industry, pp. 311–330 (2008)Google Scholar
  8. 8.
    Kofod-Petersen, A., Aamodt, A.: Contextualised Ambient Intelligence Through Case-Based Reasoning. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 211–225. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Benard, R., Bossard, C., De Loor, P.: Context’s Modeling for Participative Simulation. In: 9th Int’l Florida Artificial Intelligence Research Soc. Conf. FLAIRS 2006, pp. 613–618 (2006)Google Scholar
  10. 10.
    Muñoz-Avila, H., Cox, M.T.: Case-Based Plan Adaptation: An Analysis and Review. IEEE Intelligent Systems 23(4), 75–81 (2008)CrossRefGoogle Scholar
  11. 11.
    Ma, T., Kim, Y.-D., Ma, Q., Tang, M., Zhou, W.: Context-aware implementation based on CBR for smart home. In: IEEE Int’l Conference on Wireless And Mobile Computing, Networking And Communications WiMob 2005 (2005)Google Scholar
  12. 12.
    Nguyen, T.V., Woo, Y.C., Choi, D.: CCBR: Chaining CBR in Context-Aware Smart Home. In: 1st Asian Conf. on Intelligent Information and Database Systems (2009)Google Scholar
  13. 13.
    Kwon, O., Sadeh, N.: Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping. Decision Support Systems 37(2), 199–213 (2004)Google Scholar
  14. 14.
    Cassens, J., Kofod-Petersen, A.: Explanations and Case-Based Reasoning in Ambient Intelligent Systems. In: Int’l Workshop on CBR and Context Awareness CaCoA 2007 (2007)Google Scholar
  15. 15.
    Dong, F., Li, Z., Hu, D.H., Wang, C.-L.: A Case-Based Component Selection Framework for Mobile Context-Aware Applications. In: IEEE Int’l Symposium on Parallel and Distributed Processing with Applications ISPA 2009, pp. 366–373. IEEE Press, New York (2009)CrossRefGoogle Scholar
  16. 16.
    Zimmerman, A.: Context-awareness in user modeling: Requirements analysis for a case-based reasoning application. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 718–732. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Coutand, O., et al.: A CBR Approach for Personalizing Location-aware Services. In: Int’l Workshop on CBR and Context Awareness, CACOA 2006 (2006)Google Scholar
  18. 18.
    Sadeh, N., Gandon, F., Kwon, O.B.: Ambient Intelligence: The MyCampus Experience. Technical Report CMU-ISRI-05-123, Carnegie Mellon University (2005)Google Scholar
  19. 19.
    Vladoiu, M., Constantinescu, Z.: Framework for Building of a Dynamic User Community - Sharing of Context-Aware, Public Interest Information or Knowledge through Always-on Services. In: 10th Int’l Conf. of Enterprise Information Systems ICEIS 2008, pp. 73–87 (2008)Google Scholar
  20. 20.
    Vladoiu, M., Constantinescu, Z.: Toward Location-based Services using GPS-based Devices. In: Proceedings of Int’l Conference on Wireless Network ICWN 2008 - World Congress on Engineering WCE 2008, vol. I, pp. 799–804 (2008)Google Scholar
  21. 21.
    Vladoiu, M., Constantinescu, Z.: Learning with a Context-Aware Multiagent System. In: 9th Romanian Educational Network International Conference RoEduNet (submitted 2010)Google Scholar
  22. 22.
    Vladoiu, M., Constantinescu, Z.: Driving style analysis using data mining techniques. Int’l Journal of Computers, Communications & Control, IJCCC (2010) (to be published)Google Scholar
  23. 23.
    Shokouhi, S.V., Skalle, P., Aamodt, A., Sormo, F.: Integration of Real-time Data and Past Experiences for Reducing Operational Problems. In: Proceedings of International Petroleum Technology Conference, Doha, Qatar (2009)Google Scholar
  24. 24.
    de Mántaras, R.L., et al.: Retrieval, reuse, revision and retention in case-based reasoning. Knowledge Engineering Review 20(3), 215–240 (2005)CrossRefGoogle Scholar
  25. 25.
    Sørmo, F., Cassens, J., Aamodt, A.: Explanation in Case-Based Reasoning-Perspectives and Goals. Artificial Intelligence Review 24(2), 109–143 (2005)CrossRefGoogle Scholar
  26. 26.
    Rich, E.: User Modeling via Stereotypes. In: Readings in Intelligent User Interfaces, pp. 329–342. Morgan Kaufmann Publishers, San Francisco (1998)Google Scholar
  27. 27.
    Brézillon, P., Pomerol, J.-C.: Contextual knowledge sharing and cooperation in intelligent assistant systems. Le Travail Humain 62(3), 223–246 (1999)Google Scholar
  28. 28.
    Göker, A., Myrhaug, H.I.: User context and personalisation. In: Workshop Proceedings for the 6th European Conference on Case Based Reasoning ECCBR 2002 (2002)Google Scholar
  29. 29.
    Chaari, T., Dejene, E., Laforest, F., Scuturici, V.-M.: A comprehensive approach to model and use context for adapting applications in pervasive environments. The Journal of Systems and Software 80(12), 1973–1992 (2007)CrossRefGoogle Scholar
  30. 30.
    Bringel Filho, J., Martin, H.: Towards Awareness of Privacy and Quality of Context in Context- Based Access Control for Ubiquitous Applications. Journal on Digital Information Management 7(4), 219–226 (2009)Google Scholar
  31. 31.
    Qin, W., Suo, Y., Shi, Y.: CAMPS: A Middleware for Providing Context-Aware Services for Smart Space. In: Chung, Y.-C., Moreira, J.E. (eds.) GPC 2006. LNCS, vol. 3947, pp. 644–653. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  32. 32.
    Jih, W.-r., Hsu, J.Y.-j., Lee, T.-C., Chen, L.-I.: A Multi-agent Context-aware Service Platform in a Smart Space. Journal of Computers 18(1), 45–59 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Monica Vladoiu
    • 1
  • Jörg Cassens
    • 2
  • Zoran Constantinescu
    • 3
  1. 1.PG University of PloiestiPloiestiRomania
  2. 2.University of LübeckLübeckGermany
  3. 3.Zealsoft Ltd.BucharestRomania

Personalised recommendations