In this work, we are interested in the applications of big data in the telecommunication domain, analysing two weeks of datasets provided by Telecom Italia for Milan and Trento. Our objective is to identify hotspots which are places with very high communication traffic relative to others and measure the interaction between them. We model the hotspots as nodes in a graph and then apply node centrality metrics that quantify the importance of each node. We review five node centrality metrics and show that they can be divided into two families: the first family is composed of closeness and betweenness centrality whereas the second family consists of degree, PageRank and eigenvector centrality. We then proceed with a statistical analysis in order to evaluate the consistency of the results over the two weeks. We find out that the ranking of the hotspots under the various centrality metrics remains practically the same with the time for both Milan and Trento. We further identify that the relative difference of the values of the metrics is smaller for PageRank centrality than for closeness centrality and this holds for both Milan and Trento. Finally, our analysis reveals that the variance of the results is significantly smaller for Trento than for Milan.
Nowadays, telecom companies use widely big data in order to mine the behaviour of their customers, improve the quality of service that they provide and reduce the customers' churn. Towards this direction, demographic statistics, network deployments and call detail records (CDRs) are key factors that need to be carefully integrated in order to make accurate predictions. Though there are various open source data for the first two factors, researchers rarely have access to traffic demand data, since it is a sensitive information for the operators. Therefore, researchers need to rely on synthetic models, which do not always capture accurately large-scale mobile networks @cite_5 .
@cite_5
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In a world of open data and large-scale measurements, it is often feasible to obtain a real-world trace to fit to one's research problem. Feasible, however, does not imply simple. Taking next-generation cellular network planning as a case study, in this paper we describe a large-scale dataset, combining topology, traffic demand from call detail records, and demographic information throughout a whole country. We investigate how these aspects interact, revealing effects that are normally not captured by smaller-scale or synthetic datasets. In addition to making the resulting dataset available for download, we discuss how our experience can be generalized to other scenarios and case studies, i.e., how everyone can construct a similar dataset from publicly available information.