Refinement Strategies for Correlating Context and User Behavior in Pervasive Information Systems

Abstract

Large amounts of traces can be collected by Pervasive Information Systems, reflecting user's actions and the context in which these actions have been performed (location, date, time, network connection, etc.). This article proposes refinement strategies with different frequency measurements on contextual elements in order to better analyze the impact of these elements on the user's behavior. These strategies are based on data mining and Formal Concept Analysis and used to refine input data in order to identify the context elements that have a strong impact on user behaviors. We go further on context analysis by cognizing FCA with semantic distance measures calculated based on a context ontology. The proposed context analysis is further on evaluated in experiments with real data. The novelties of this work lies on these refinement strategies which can lead to a better understanding of context impact. Such understanding represents an important step towardspersonalization and recommendation features.