A hybrid architecture to enrich context awareness through data correlation

Context awareness brings new challenges, and an important one is how applications can manipulate the contextual data stored in more than one model. In this research, we propose HACCD, a context-aware architecture to process information based on hybrid models. HACCD is designed to provide context awareness considering different stages: (i) acquisition of context; (ii) preprocessing stage; (iii) context processing with a hybrid reasoning strategy; (iv) data storage with the support of three database models; (v) repository communication that enable access to contextual information; and, (vi) correlation approach based on compositional rules that allow the combination of data stored in distinct models. To validate our architecture we designed and tested within some scenarios based on information security. The obtained results showed that the possibility of correlating data from different natures could help to identify richer situations, thus improving decision-making.