M-Atlas is a mobility querying and data mining system centered onto the concept of spatio-temporal data. Besides the mechanisms for storing and querying trajectory data, M-Atlas has mechanisms for mining trajectory patterns and models that, in turn, can be stored and queried. The knowledge discovery process can help the user to answer to typical mobility analysis questions like: What are the frequent patterns of people’s travels? How do big attractors and extraordinary events affect the mobility? How can areas of dense traffic be predicted in the near future? How can traffic jams and congestions be characterized? M-Atlas is equipped with a querying and mining language that makes this analytical process possible, and it provides the mechanisms to master the complexity of transforming raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory but it is able to handle other kinds of data such as positional data (e.g., Call Data Records). The mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further analyses and mining, the incremental mining strategies to address scalability.