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The technologies of mobile communications\ud pervade our society and wireless networks sense the movement\ud of people, generating large volumes of mobility data,\ud such as mobile phone call records and Global Positioning\ud System (GPS) tracks. In this work, we illustrate the striking\ud analytical power of massive collections of trajectory data in\ud unveiling the complexity of human mobility. We present the\ud results of a large-scale experiment, based on the detailed trajectories\ud of tens of thousands private cars with on-board GPS\ud receivers, tracked during weeks of ordinary mobile activity.\ud We illustrate the knowledge discovery process that, based on\ud these data, addresses some fundamental questions of mobility\ud analysts: what are the frequent patterns of people’s travels?\ud How big attractors and extraordinary events influence mobility?\ud How to predict areas of dense traffic in the near future?\ud How to characterize traffic jams and congestions? We also\ud describe M-Atlas, the querying and mining language and system\ud that makes this analytical process possible, providing the\ud mechanisms to master the complexity of transforming raw\ud GPS tracks into mobility knowledge. M-Atlas is centered\ud onto the concept of a trajectory, and the mobility knowledge\ud discovery process can be specified by M-Atlas queries that\ud realize data transformations, data-driven estimation of the\ud parameters of the mining methods, the quality assessment\ud of the obtained results, the quantitative and visual exploration\ud of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further\ud analyses and mining, and the incremental mining strategies\ud to address scalability

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Community Discovery in complex networks is the problem of detecting, for each node of the network, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive, or, more in general, similar, according to a similarity function. So far, the problem has been widely studied in monodimensional networks, i.e. networks where only one connection between two entities may exist. However, real networks are often multidimensional, i.e., multiple connections between any two nodes may exist, either reflecting different kinds of relationships, or representing different values of the same type of tie. In this context, the problem of Community Discovery has to be redefined, taking into account multidimensional structure of the graph. We define a new concept of community that groups together nodes sharing memberships to the same monodimensional communities in the different single dimensions. As we show, such communities are meaningful and able to group nodes even if they might not be connected in any of the monodimensional networks. We devise ABACUS (frequent pAttern mining-BAsed Community discoverer in mUltidimensional networkS), an algorithm that is able to extract multidimensional communities based on the extraction of frequent closed itemsets from monodimensional community memberships. Experiments on two different real multidimensional networks confirm the meaningfulness of the introduced concepts, and open the way for a new class of algorithms for community discovery that do not rely on the dense connections among nodes.