Granular Best Match Algorithm for Context-Aware Computing Systems

In order to be context-aware, a system or application should adapt its behavior according to current context, changing over time. Current context is acquired by some context provision mechanisms like sensors or other applications. After acquiring current context, this information should be matched against the previously defined context sets. In this paper, a granular best match algorithm dealing with the subjective, fuzzy, multi-granular and multi-dimensional characteristics of contextual information is introduced. The CAPRA - context-aware personal reminder agent tool is used to show the applicability of the new context matching algorithm. The obtained outputs showed that proposed algorithm produces the results which are more sensitive to the user´s intention, and more adaptive to the aforementioned characteristics of the contextual information than the traditional exact match method