A methodology for preference-based personalization of contextual data

The widespread use of mobile appliances, with limitations in terms of storage, power, and connectivity capability, requires to minimize the amount of data to be loaded on user’s devices, in order to quickly select only the information that is really relevant for the users in their current contexts: in such a scenario, specific methodologies and techniques focused on data reduction must be applied. We propose an extension to the data tailoring approach of Context-ADDICT, whose aim is to dynamically hook and integrate heterogeneous data to be stored on small, possibly mobile devices. The main goal of our extension is to personalize the context-dependent data obtained by means of the Context-ADDICT methodology, by allowing the user to express preferences that specify which data s/he is more interested in (and which not) in each specific context. This step allows us to impose a partial order among the data, and to load only the top (most preferred) portion of the data chunks. A running example is used to better illustrate the approach.