Behavioral patterns can be extracted from the traces of past human activities through a semantic- enriched knowledge discovery process.
Here, positioning data are first transformed in semantically enriched trajectory data stored in a database. Then, mobility patterns, the most common movements emerging from data, are computed with data mining algorithms. A further semantic enrichment step is needed to give context-dependent meaning to the discovered patterns. New challenging research topics result from this work.

is an industrial R&D project funded by Unicoop, one of largest italian retail companies. KDDLab created a very large sales data warehouse, equipped with advanced BI solutions for strategic marketing and customer care based on data mining predictive analytics.

DATSIM aims at providing an entirely new and highly detailed spatial-temporal microsimulation methodology for human mobility, grounded on massive amounts of Big data of various types and from various sources, e.g. GPS, mobile phones and social networking sites, with the goal to forecast the nation-wide consequences of a massive switch to electric vehicles, given the intertwined nature of mobility and power distribution networks.