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The recent technological advances on telecommunications create a new reality on mobility sensing. Nowadays, we live in an era where ubiquitous digital devices are able to broadcast rich information about human mobility in real-time and at a high rate. Such fact exponentially increased the availability of large-scale mobility data which has been popularized in the media as the new currency, fueling the future vision of our smart cities that will transform our lives. The reality is that we just began to recognize significant research challenges across a spectrum of topics. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders on building knowledge discovery pipelines over such data sources. However, such availability also raises privacy issues that must be considered by both industrial and academic stakeholders on using these resources. [ABSTRACT FROM AUTHOR]

The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists. [ABSTRACT FROM AUTHOR]

Recently, professional team sport organizations have invested their resources to analyze their own and opponents' performance. So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists. Analyzing football is hard because of its complexity, number of events in each match, and constant flow of circulation of the ball. Finding roles of players with the purpose of analyzing the performance of a team or making a meaningful comparison between players is crucial. In this article, an automatic big data clustering method, based on a swarm intelligence algorithm, is proposed to automatically cluster the data set of players' performance centers in different matches and extract different kinds of roles in football. The proposed method created using particle swarm optimization algorithm has two phases. In the first phase, the algorithm searches the solution space to find the number of clusters and, in the second phase, it finds the positions of the centroids. To show the effectiveness of the algorithm, it is tested on six synthetic data sets and its performance is compared with two other conventional clustering methods. After that, the algorithm is used to find clusters of a data set containing 93,000 objects, which are the centers of players' performance in about 4900 matches in different European leagues.