___________________________________________________________________Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for i) constructing trajectories from movement tracks, ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the paper surveys the new privacy issues that rise due to the semantic aspects of trajectories.

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

Several works have been proposed in the last few years for raw trajectory data analysis, and some attempts have been made to define trajectories from a more semantic point of view. Semantic trajectory data analysis has received significant attention recently, but the formal definition of semantic trajectory, the set of aspects that should be considered to semantically enrich trajectories and a conceptual data model integrating these aspects from a broad sense is still missing. This article presents a semantic trajectory conceptual data model named CONSTAnT, which defines the most important aspects of semantic trajectories. We believe that this model will be the foundation for the design of semantic trajectory databases, where several aspects that make a trajectory "semantic" are taken into account. The proposed model includes the concepts of semantic subtrajectory, semantic points, geographical places, events, goals, environment and behavior, to create a general concept of semantic trajectory. The proposed model is the result of several years of work by the authors in an effort to add more semantics to raw trajectory data for real applications. Two application examples and different queries show the flexibility of the model for different domains.In order to clarify the proposed conceptual model for semantic trajectories, this section defines some basic concepts and presents the closest related works. CONSTAnT -A Conceptual Data Model for Semantic Trajectories 67

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For many years trajectory data have been treated as sequences of space-time points or stops and moves. However, with the explosion of the Internet of Things and the flood of big data generated on the Internet, such as weather chan-

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The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple‐aspect trajectories, where mobility data are enriched with several semantic dimensions, current state‐of‐the‐art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple‐aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple‐aspect trajectories.