Promoting Efficiency and Separation of Concerns through a Hybrid Model Based on Ontologies for Context-Aware Computing

Abstract

Several projects in context-aware computing have adopted ontologies for modeling context information, due to their powerful constructors for modeling highly dependent concepts and their mechanisms for describing complex inferences. However, the implementation of ontologies introduces some scalability and performance problems for context-aware systems, which usually have to handle high-volume of distributed information. To circumvent this drawback, some research efforts have proposed the adoption of hybrid context models. This paper presents a hybrid context model for the MoCA, a middleware for developing and deploying context-aware collaborative applications for mobile users