Towards a Better Understanding of Context and Context-Awareness
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
MobiPADS: A Reflective Middleware for Context-Aware Mobile Computing
IEEE Transactions on Software Engineering
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
EFoX: a scalable method for extracting frequent subtrees
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
EXiT-B: a new approach for extracting maximal frequent subtrees from XML data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Research and implementation of the context-aware middleware for controlling home appliances
IEEE Transactions on Consumer Electronics
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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.