Paolo Cintia

Learn More
The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not(More)
Sports analytics in general, and football (soccer in USA) analytics in particular, have evolved in recent years in an amazing way, thanks to automated or semi-automated sensing technologies that provide high-fidelity data streams extracted from every game. In this paper we propose a data-driven approach and show that there is a large potential to boost the(More)
The availability of inexpensive tracking devices, such as GPS-enabled devices, gives the opportunity to collect large amounts of trajectory data from vehicles. In this context, we are interested in the problem of generating the traffic information in time-dependent networks using this kind of data. This problem is not trivial since several works in(More)
—The recent emergence of the so called online social fitness constitutes a good proxy to study the patterns underlying success in sport. Through these platforms, users can collect, monitor and share with friends their sport performance, diet, and even burned calories, giving an unprecedented opportunity to answer very fascinating questions: What are the(More)
In the era of the proliferation of Geo-Spatial Data, induced by the diffusion of GPS devices, the map matching problem still represents an important and valuable challenge. The process of associating a segment of the underlying road network to a GPS point gives us the chance to enrich raw data with the semantic layer provided by the roadmap, with all(More)
Real world events are intrinsically dynamic and analytic techniques have to take into account this dynamism. This aspect is particularly important on complex network analysis when relations are channels for interaction events between actors. Sensing technologies open the possibility of doing so for sport networks, enabling the analysis of team performance(More)
In the era of Big Data a huge amount of information are available from every single citizen of our hyper-connected world. A simple smartphone can collect data with different kinds of information: a big part of these are related to mobility. A smartphone is connected to networks, such as GSM, GPS, Internet (and then social networks): each of them can provide(More)
The recent emergence of the so called online social fitness constitutes a good proxy to study the patterns underlying success in sport. Through these platforms, users can collect, monitor and share with friends their sport performance, diet, and even burned calories, giving an unprecedented opportunity to answer very fascinating questions: What are the main(More)
  • 1