Methodology for knowledge extraction from mobility big data

The spread of mobile devices with several sensors, together with mo-bile communication, provides huge volumes of real-time data (big data) about users’ mobility habits, which should be correctly analysed to extract useful knowledge. In our research we explore a data mining approach based on a Naïve Bayes (NB) classifier applied to different sources of big data. To achieve this goal, we propose a methodology based on four processes that collects data and merges different data sources into pre-defined data classes. We can apply this methodology to different big data sources and extract a diversity of knowledge that can be applied to the development of dedicated applications and decision processes in the area of intelligent transportation systems, such as route advice, CO2 emissions reduction through fuel savings, and provision of smart advice for public transportation usage

Data mining by means of generalized patterns

The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces