A great challenge in context-aware computing is dealing with the heterogeneity and volume of sensors data. A problem regarding that scenario is to notify context-aware applications, which have distinct interests of context events in terms of volume, semantic and complexity, in an efficient and relevant manner. Aiming to solve this problem, this research focuses on a new approach for filtering semantic context towards supporting context dissemination. This mechanism is to be aligned with the reasoning capabilities of a context-aware solution and also be maintainable and extensible to efficiently support changes in an ontological model. A performance evaluation is carried out in a simulated scenario of vital signs monitoring in Intensive Care Units and wards. Hermes Interpreter's behaviour is analysed when dealing with filters of different complexities and also an increasing number of subscribers per vital sign. Results demonstrate the high cost of the semantic filtering mechanism in comparison with pure context reasoning activities.

filtering, semantics, context dissemination, experimentation