Mining Context-Aware Preferences on Relational and Sensor Data

The increasing amount of available digital data motivates the development of techniques for the management of the information overload which risks to actually reduce people’s knowledge instead of increasing it. Research is concentrating on topics related to the problem of filtering/suggesting a subset of available information that is likely to be of interest to the user, besides this subset may vary and is often determined by the context the user is currently in. We cannot actually expect only a collaborative approach, where users manually specify the long list of preferences that might be applied to all available data; that is why in this paper we propose a preliminary methodology, described by using a realistic running example, that tries to combine the following research topics: context-awareness, data mining, and preferences. In particular, data mining is used to infer contextual preferences from the previous user’s querying activity on static data and on available dynamic values coming from sensors.