Towards Context-Aware Data Management for Ambient Intelligence

Abstract. Ambient Intelligence (AmI) is a vision of future Information Society, where people are surrounded by an electronic environment which is sensitive to their needs, personalized to their requirements, anticipa-tory of their behavior, and responsive to their presence. It emphasizes on greater user-friendliness, user-empowerment, and more eective ser-vice support, with an aim to make people's daily activities more con-venient, thus improving the quality of human life. To make AmI real, eective data management support is indispensable. High-quality infor-mation must be available to any user, anytime, anywhere, and on any lightweight device. Beyond that, AmI also raises many new challenges related to context-awareness and natural user interaction, entailing us to re-think current database techniques. The aim of this paper is to address the impact of AmI, particularly its user-centric context-awareness requirement on data management strate-gies and solutions. We rst provide a multidimensional view of database access context. Taking diverse contextual information into account, we then present ve context-aware data management strategies, using the most fundamental database operation- context-aware query request as a case in point. We execute the proposed strategies via a two-layered infrastructure, consisting of public data manager(s) and a private data manager. Detailed steps of processing a context-aware query are also described in the paper.