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Context-aware applications adapt their behavior based on collected contexts. However, contexts can be inaccurate due to sensing noise, which might cause applications to misbehave. One promising approach is to check contexts against consistency constraints at runtime, so as to detect context inconsistencies for applications and resolve them in time. The checking is typically immediate upon each collected...
Pervasive applications are involving more and more autonomous computing and communicating devices, augmented with the abilities of sensing and controlling the logical/physical environment. To enable context-awareness for such applications, we are challenged by the intrinsic asynchrony of the computing environment. Predicate detection is a well studied technique dedicated to detecting global predicates...
Contextual information is proven helpful to recommender system. And context-aware recommender system(CARS) has been applied in various applications. To improve the accuracy of context-aware recommendation and make recommender application development easier, we develop a lightweight software framework named ConRec, which introduces a dynamic context oriented approach to extend traditional reduction...
Detecting and resolving context inconsistency is critical to pervasive computing applications and infrastructures. Context inconsistency occurs when an application perceives contexts that breach predefined consistency constraints. This can drive an application to behave abnormally or even cause failure. Existing work commonly assumes the presence of a single application suffering from context inconsistency,...
Context-awareness is a key issue in pervasive computing. Context-aware applications are prone to the context consistency problem, where applications are confronted with conflicting contexts and cannot decide how to adapt themselves. In pervasive computing environments, users are often willing to accept certain degree of context inconsistency, as long as it can reduce the consistency maintenance cost,...
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