Enhancing Alignment Based Context Prediction by Using Multiple Context Sources: Experiment and Analysis

Abstract

Context aware applications are reactive, they adapt to an entity’s context when the context has changed. In order to become proactive and act before the context actually has changed future contexts have to be predicted. This will enable functionalities like preloading of content or detection of future conflicts. For example if an application can predict where a user is heading to it can also check for train delays on the user’s way. So far research concentrates on context prediction algorithms that only use a history of one context to predict the future context. In this paper we propose a novel multidimensional context prediction algorithm and we show that the use of multidimensional context histories increases the prediction accuracy. We compare two multidimensional prediction algorithms, one of which is a new approach; the other was not yet experimentally tested. In theory, simulation and a real world experiment we verify the feasibility of both algorithms and show that our new approach has at least equal or better reasoning accuracy.