ORACON: an adaptive model for contexts prediction

Contexts prediction has been receiving considerable attention in the last years. Furthermore, this area seems to be the next logical step in context-aware computing, which, until a few years ago, had been concerned more with the present and the past temporal dimensions. There are many works regarding models for contexts prediction. Nevertheless, most of them employ the same algorithm for all cases. In other words, we did not find any approach that automatically decides the best prediction method according to the situation. Therefore, we propose the ORACON model. ORACON adapts itself in order to apply the best algorithm to the case. Moreover, the model supports other important aspects of ubicomp, such as, context formal representation and privacy. In this thesis, we describe the ORACON design and evaluate the model through two experiments, one using real data and the other employing simulated information.