A flood of data pertinent to moving objects is available today, and will be more in the near future,
particularly due to the automated collection of data from personal devices such as mobile phones and
other location-aware devices. Such wealth of data, referenced both in space and time, may enable
novel classes of applications of high societal and economic impact, provided that the discovery of
consumable and concise knowledge out of these raw data is made possible.
The fundamental hypothesis is that it is possible, in principle, to aid citizens in their mobile
activities by analysing the traces of their past activities by means of data mining techniques. For
instance, behavioural patterns derived from mobile trajectories may allow inducing traffic flow
information, capable to help people travelling efficiently, to help public administrations in traffic related
decision making for sustainable mobility and security management. Behavioral patterns can be extracted through a knowledge discovery process where positioning
data collected from mobile devices are first transformed in semantically enriched trajectory data stored
in a database. Then, these data are loaded in a data warehouse and analysed with OLAP operations
that allow summarization of the trajectories features. Mobility patterns, the most common movements
emerging from data, are computed with suitable spatio-temporal data mining algorithms. A further
semantic enrichment step is needed to give context-dependent meaning to the discovered patterns.