A principled approach to context schema evolution in a data management perspective

Context-aware data tailoring studies the means for the system to furnish the users, at any moment, only with the set of data which is relevant for their current context. These data may be from traditional databases, sensor readings, environmental information, close-by people, points of interest, etc. To implement context-awareness, we use a formal representation of a conceptual context model, used to design the context schema, which intensionally represents all the contexts in which the user may be involved in the considered application scenario.Following this line of thought, in this paper we develop a formal approach and the corresponding strategy to manage the evolution of the context schema of a given context-aware application, when the context perspectives initially envisaged by the system designer are not applicable any more and unexpected contexts are to be activated. Accordingly, when the context schema evolves also the evolution of the corresponding context-aware data portions must be taken care of. The aim of this paper is thus to provide the necessary conceptual and formal notions to manage the evolution of a context schema in the perspective of data tailoring: after introducing a set of operators to manage evolution and proving their soundness and completeness, we analyze the impact that context evolution has on the context-based data tailoring process. We then study how sequences of operator applications can be optimized and finally present a prototype validating the feasibility of the approach. HighlightsThe paper proposes a formal approach to manage the evolution of context schemas over time.Context schemas describe the possible contexts in a given scenario.The evolution strategy is founded on a sound and complete set of operators.The impact of the evolution on the context-aware data tailoring process is also studied.A prototype ensures the feasibility and efficiency of the approach.