An efficient context modeling and reasoning system in pervasive environment: using absolute and relative context filtering technology

Authors:
Xin Lin;Shanping Li;Jian Xu;Wei Shi;Qing Gao
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, P.R.China;College of Computer Science, Zhejiang University, Hangzhou, P.R.China;College of Computer Science, Hangzhou Dianzi University, Hangzhou, P.R.China;College of Computer Science, Zhejiang University, Hangzhou, P.R.China;College of Computer Science, Zhejiang University, Hangzhou, P.R.China
Venue:
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Year:
2005

Citing 7
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Abstract

Challenges revealed in designing efficient context modeling and reasoning systems in pervasive environment are due to the overwhelming contextual information in such environment. In this paper we aim at designing an attribute-based context filtering technology (ACMR) to improve the performance of context processing. Two metrics, absolute and relative attributes, are proposed in our work to analyze the contextual information. ACMR only processes the application-related contextual information rather than all the available contextual information to prevent context-aware applications from being distracted by trashy contexts. Additionally, to encourage the reuse and standardization, contexts ontology TORA is developed to model the contexts and their absolute attributes in the pervasive environment. Experiments about ACMR system demonstrate its higher performance than those of previous systems.