Abstract
In recent years, network operators are receiving an outsize amount of data due to the increasing number of mobile network subscribers, network services and device signalling. This trend increases with the deployment of 5G that will provide advanced connectivity to wireless devices and develop new services. Network analytics must allow telecommunications operators to improve their services and the infrastructure extracting useful information from large amounts of data. A methodology based on orthogonal projections was developed in order to analyze the network information and facilitate the management and the operations to network providers. In the current study, different key points selection algorithms are investigated in order to make a quantitative and qualitative evaluation and analyze the performance of those algorithms which use different approaches to select these points, which will be utilized in the methodology. A novel synthetic data set has also been developed to statistically evaluate the effect of the key points selection algorithms in the clustering, as well as, measure the performance of the aforementioned methodology. Finally, these key points selection algorithms are used in a real scenario to evaluate the impact of the different approaches in the analysis.
Highlights
Nowadays, it is estimated that networks generate and exchange around 2.5 exabytes of data daily [1]
A set of experiments based on the described methodology with the signatures extracted from the three key points algorithms are introduced in Section III in order to provide an analytical comparison
The key points selection algorithm based on Orthogonal Subspace Projections [7]–[13] aims to find those extreme points in the f -dimensional space formed by the feature arrays collected for each sub-area of the data cube
Summary
It is estimated that networks generate and exchange around 2.5 exabytes of data daily [1]. Two new synthetic data sets have been developed in order to analyze in-depth the methodology proposed in previous works [7], [8] and to provide a valid reference for numerical measures. These data sets are based in a fractal algorithm that allows creating different scenarios and intervals that simulate the geographic distribution of urban regions, similar to the information contained in a real CDR data set. A set of experiments based on the described methodology with the signatures extracted from the three key points algorithms are introduced in Section III in order to provide an analytical comparison.
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