Country-Scale Exploratory Analysis of Call Detail Records Through the Lens of Data Grid Models

Abstract

International audienceCall Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network: the source, destination , date and time of calls. CDRs data analysis has received much attention in the recent years since it might reveal valuable information about human behavior. It has shown high added value in many application domains like e.g., communities analysis or network planning. In this paper, we suggest a generic methodology based on data grid models for summarizing information contained in CDRs data. The method is based on a parameter-free estimation of the joint distribution of the variables that describe the calls. We also suggest several well-founded criteria that allows one to browse the summary at various granularities and to explore the summary by means of insightful visualizations. The method handles network graph data, temporal sequence data as well as user mobility data stemming from original CDRs data. We show the relevance of our methodology on real-world CDRs data from Ivory Coast for various case studies, like network planning strategy and yield management pricing strategy

Mobile Demand Profiling for Cellular Cognitive Networking

“…While we acknowledge that different options might be envisioned, this spatial aggregation is intuitive, as it is based on topological criteria. Additionally, it has been proved in [38] that the administrative division of Abidjan corresponds to similar typical behaviors of the different base stations described in the D4D dataset. The available dataset only contains voice call volumes, and therefore all our results refer to voice traffic.…”

Revisiting enumerative two-part crude MDL for Bernoulli and multinomial distributions (Extended version)

“…Simple models such as Bernoulli or mainly multinomial distributions are important because they are easier to analyze theoretically and useful in many applications. For example, the multinomial distribution has been used as a building block in more complex models, such as naive Bayes classifiers (Mononen and Myllymäki, 2007), Bayesian networks (Roos et al, 2008), decision trees (Voisine et al, 2009) or coclustering models (Boullé, 2011;Guigourès et al, 2015). These models involve up to thousands of multinomials blocks, some of them with potentially very large numbers of occurrences and outcomes.…”

“…For example, the text × word coclustering of the 20newsgroup dataset described in (Boullé, 2011) exploits a main multinomial block with around two millions words (occurrences) distributed on 200,000 coclusters (outcomes). In (Guigourès et al, 2015), half a billion call detail records (occurrences) are distributed on one million coclusters (outcomes). These various and numerous applications critically rely on the use of effective and efficient MDL code lengths to get a robust and accurate summary of the data.…”