English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we are interested in the applications of big data in thetelecommunication domain, analysing two weeks of datasets provided by TelecomItalia for Milan and Trento. Our objective is to identify hotspots which areplaces with very high communication traffic relative to others and measure theinteraction between them. We model the hotspots as nodes in a graph and thenapply node centrality metrics that quantify the importance of each node. Wereview five node centrality metrics and show that they can be divided into twofamilies: the first family is composed of closeness and betweenness centralitywhereas the second family consists of degree, PageRank and eigenvectorcentrality. We then proceed with a statistical analysis in order to evaluatethe consistency of the results over the two weeks. We find out that the rankingof the hotspots under the various centrality metrics remains practically thesame with the time for both Milan and Trento. We further identify that therelative difference of the values of the metrics is smaller for PageRankcentrality than for closeness centrality and this holds for both Milan andTrento. Finally, our analysis reveals that the variance of the results issignificantly smaller for Trento than for Milan.

Node Centrality Metrics for Hotspots Analysis in Telecom Big Data