ABSTRACT:
Independent, heterogeneous, distributed, sometimes tran-
sient and mobile data sources produce an enormous
amount of information that should be semantically inte-
grated and filtered, or, as we say, tailored, based on the
users’ interests and context. We propose to exploit knowl-
edge about the user, the adopted device, and the environ-
ment – altogether called context – to the end of informa-
tion tailoring. This paper presents the Context Dimension
Tree, a context model which is the basis for solving the
information tailoring problem, along with its role in the
framework of the Context-ADDICT architecture.