A survey of context models and a preliminary methodology for context driven data view definition

Very often we face the need of extracting appropriate data views from an integrated representation of the information space, and of defining, a posteriori, personalized views for the information stakeholders. The peer to peer scenario of the ESTEEM project introduces another interesting motivation behind the desire to define customized data views of a semantic community information, tailored on the peer’s interests. This report presents our context model, called Context Dimension Tree, which plays a fundamental role in tailoring the information space according to the user’s information needs, as well as an analysis of relevant features of context models, later used for classification and comparison.