A context-aware preference model for database querying in an Ambient Intelligent environment

Users' preferences have traditionally been exploited in query personalization to better serve their information needs. With the emerging ubiquitous computing technologies, users will be situated in an Ambient Intelligent (AmI) environment, where users' database access will not occur at a single location in a single context as in the traditional stationary desktop computing, but rather span a multitude of contexts like office, home, hotel, plane, etc. To deliver personalized query answering in this environment, the need for context-aware query preferences arises accordingly. In this paper, we propose a knowledge-based context-aware query preference model, which can cater for both \emph{pull} and \emph{push} queries. We analyze requirements and challenges that AmI poses upon such a model and discuss the interpretation of the model in the domain of relational databases. We implant the model on top of a traditional DBMS to demonstrate the applicability and feasibility of our approach.