Abstract
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.
This work was supported in part by the University IT Research Center Project (ITRC) and by the Ubiquitous Autonomic Computing and Network Project, 21st Century Frontier R&D Program, funded by the Korean Ministry of Information and Communication.
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Paik, J., Youn, H.Y., Kim, U.M. (2006). Extraction of Implicit Context Information in Ubiquitous Computing Environments. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751632_50
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DOI: https://doi.org/10.1007/11751632_50
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