Research Challenge of Locally Computed Ubiquitous Data Mining
Author:
Cayci Aysegul1, Gomes João Bártolo2, Zanda Andrea2, Menasalvas Ernestina2, Eibe Santiago2
Affiliation:
1. Sabanci University, Turkey 2. Universitad Politecnica, Spain
Abstract
Advances in wireless, sensor, mobile and wearable technologies present new challenges for data mining research on providing mobile applications with intelligence. Autonomy and adaptability requirements are the two most important challenges for data mining in this new environment. In this chapter, in order to encourage the researchers on this area, we analyzed the challenges of designing ubiquitous data mining services by examining the issues and problems while paying special attention to context and resource awareness. We focused on the autonomous execution of a data mining algorithm and analyzed the situational factors that influence the quality of the result. Already existing solutions in this area and future directions of research are also covered in this chapter.
Reference38 articles.
1. Mining top-k frequent patterns in the presence of the memory constraint 2. Davidyuk, O., Riekki, J., Rautio, V., & Sun, J. (2004). Context-aware middleware for mobile multimedia applications. In Proceedings of the 3rd international Conference on Mobile and Ubiquitous Multimedia (College Park, Maryland, October 27 - 29, 2004). MUM '04, vol. 83. ACM, New York 213-220. 3. Fook, V., Tee, J., Yap, K., Wai, A., Maniyeri, J., Jit, B., & Lee, P. (2007). Smart mote-based medical system for monitoring and handling medication among persons with Dementia.In Proc. ICOST 2007, LNCS 4541, pp.54-62. 4. Franklin, S., & Graesser, A. (1997). Is it an agent, or just a program?: A taxonomy for autonomous agents. In Proceedings of the Workshop on Intelligent Agents Iii, Agent theories, Architectures, and Languages (August 12 - 13, 1996). J. P. Müller, M. Wooldridge, and N. R. Jennings, Eds. Lecture Notes In Computer Science, vol. 1193. Springer-Verlag, London, 21-35. 5. Gaber, M. M., Krishnaswamy, S., and Zaslavsky, A. (2003). Adaptive mining techniques for data streams using algorithm output granularity. In Proceedings of the Australasian Data Mining Workshop (AusDM 2003) Held in conjunction with the 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, (December 2003). Lecture Notes in Computer Science (LNCS). Springer Verlag.
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