Big-Thick Data

We integrate Big-Thick Data as highly contextualized data by modeling Observation Contexts. Observation Contexts are based on two main components: one or more users’ Personal Contexts and a Reference Context. A personal context encodes a person’s subjective view of the world, e.g., where she is, what she is doing, who she is with, and her mood. We model personal contexts, in time, as Personal Big-Thick Data obtained by integrating Personal Big-Data, e.g., sensor or social media data, with user provided descriptions of the current situation, e.g., human answers to machine questions and human self-reports. A reference context provides a user-independent objective all-encompassing view of a third-party observer. It keeps track of the environment within which users are operating, defined in terms of a reference observation period and a reference location, e.g., one day and the city of Trento. The reference context can be built out of any type of spatio-temporal (big) data, e.g., OpenStreetMap (OSM) or the Italian Spatial Data portal.

The observation context is built by composing (a part of) the objective reference context and with (parts of) one or more subjective personal contexts based on shared identifying information, e.g., names, identifiers, spatio-temporal coordinates. We call this process Context Unification. Context unification is a flexible process depending on the specific purpose, thus allowing for different types of questions about the users’ behaviour and the world around them and, also, for multiple different answers to the same question.