EXEHDA-HM: A compositional approach to explore contextual information on hybrid models

Abstract The current proposals of hybrid context modeling bring new challenges, an important one is how applications can access and process data stored on these models. Thinking about that, this paper proposes a solution to deal with this challenge through a compositional approach that explores the context information on hybrid models, called EXEHDA-HM. The proposed approach stands out by the design of a repository that supports three database models and by the compositional processing strategy based on rules. In our proposal, the applications can combine data stored on different bases in a single rule, which could enhance the identification of contextual situations. For the evaluation we designed and implemented some case studies on information security area, exploring the hybrid repository composed of relational, non-relational, and triple storage models. Our results demonstrate that was possible to identify richer situations with the data composition across more than one model and there are situations that can only be found through this composition.