Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
A Multiple Attribute Utility Theory Approach to Ranking and Selection
Management Science
Towards a Theory of Context Spaces
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Human-Computer Interaction
A middleware for context-aware agents in ubiquitous computing environments
Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware
An approach to data fusion for context awareness
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
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Reasoning under uncertainty is a key challenge in context aware pervasive systems. In this paper we propose R-CS a situation based context reasoning model that employs ranking technique to rank and order context attributes. Using the proposed ranking technique and available context information, we compute dynamic situation spaces (a collection of contextual attributes that best represent a real world situation) We also propose and incorporate multilevel hierarchical contextual regions into R-CS that enables situation reasoning to be based on one or more dependent context attributes. We present a theoretical approach to compute importance and relevance of newly discovered context attributes which are not defined within the situation space definition by employing the approach of investigating similar neighboring situation spaces. R-CS builds on context spaces theory, a context model based on situation reasoning. We have implemented the proposed algorithms/approaches into R-CS and have validated them by evaluating against context spaces reasoning model.