Extraction of Implicit Context Information in Ubiquitous Computing Environments

  • Juryon Paik
  • Hee Yong Youn
  • Ung Mo Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3983)


The evolution of low-cost, networked sensors, often directly internet-enabled, is bringing truly ubiquitous smart environments into daily life. The more ubiquitous middleware platform is intelligent, the greater context information flood problem has been caused. Hence, there have been increasing demands for efficient methods of discovering desirable knowledge from a large collection of context data. But unfortunately, current ubiquitous middleware platforms do not employ appropriate data mining techniques to meet such growing demands. Therefore, this paper aims to propose a new design of ubiquitous middleware platform that enhances context awareness in evolving pervasive environments. We achieve this goal first by incorporating a mining module into our previously suggested middleware platform CALM (Component-based Autonomic Layered Middleware) and then by instantiating the module with an efficient mining algorithm.


Context Factor Context Information Minimum Support Context Data Mining Module 


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  1. 1.
    Asai, T., Abe, K., Kawasoe, S., Arimura, H., Sakamoto, H., Arikawa, S.: Efficient substructure discovery from large semi-structured data. In: Proceedings of the 2nd SIAM International Conference on Data Mining, pp. 158–174 (2002)Google Scholar
  2. 2.
    Brown, P.J.: The Stick-e Document: a Framework for Creating Context-Aware Applications, pp. 259–272. Electronic Publishing (1996)Google Scholar
  3. 3.
    Chan, A.T.S., Chuang, S.-N.: MobiPADS: A Reflective Middleware for Context-Aware Mobile Computing. IEEE Transactions on Software Engineering 29(12), 1072–1085 (2003)CrossRefGoogle Scholar
  4. 4.
    Choi, J., Shin, D., Shin, D.: Research and Implementation of the Context-Aware Middleware for Controlling Home Appliances. IEEE Transactions on Consumer Electronics 51(1), 301–306 (2005)CrossRefGoogle Scholar
  5. 5.
    Dey, A.K., Abowd, G.D.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Lawson, J., Raines, R., Baldwin, R., Hartrum, T., Littlejohn, K.: Modeling Adaptive Middleware and Its Application to Military Tactical Datalinks. In: IEEE Military Communications Conference, pp. 975–980 (2004)Google Scholar
  7. 7.
    Paik, J., Shin, D.R., Kim, U.M.: EFoX: a Scalable Method for Extracting Frequent Subtrees. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 813–817. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Paik, J., Won, D., Fotouhi, F., Kim, U.M.: EXiT-B: A new approach for extracting maximal frequent subtrees from XML data. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 1–8. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Salber, D., Dey, A.K., Orr, R.J., Abowd, G.D.: Designing for Ubiquitous Computing: A Case Study in Context Sensing. GVU Technical Report GIT-GVU 99-29, http://smartech.gatech.edu:8282/dspace/handle/1853/3396
  10. 10.
    Vajirkar, P., Singh, S., Lee, Y.: Context-aware data mining framework for wireless medical application. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 381–391. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Shilit, B., Adams, N., Want, R.: Context-Aware computing applications. In: Proceedings of IEEE Workshop on Mobile Computing Systems and Applications, pp. 85–90 (1994)Google Scholar
  12. 12.
    You, Y.K., Han, S., Song, S.K., Youn, H.Y.: CALM: An Intelligent Agent-based Middleware Architecture for Community Computing. In: The 4th Workshop on Adaptive and Reflective Middleware (2005)Google Scholar
  13. 13.
    Zaki, M.J.: Efficiently mining frequent trees in a forest. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 71–80 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Juryon Paik
    • 1
  • Hee Yong Youn
    • 1
  • Ung Mo Kim
    • 1
  1. 1.Department of Computer EngineeringSungkyunkwan UniversitySuwon, Gyeonggi-doRepublic of Korea

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