Abstract
In telecommunication business, a major investment goes into the infrastructure and its maintenance, while business revenues are proportional to how big, good, and well-balanced the customer base is. In our previous work we presented a data-driven analytic strategy based on combinatorial optimization and analysis of the historical mobility designed to quantify the desirability of different geo-demographic segments, and several segments were recommended for a partial reduction. Within a segment, clients are different. In order to enable intelligent reduction, we introduce the term infrastructure-stressing client and, using the proposed method, we reveal the list of the IDs of such clients. We also have developed a visualization tool to allow for manual checks: it shows how the client moved through a sequence of hot spots and was repeatedly served by critically loaded antennas. The code and the footprint matrix are available on the SourceForge.
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Acknowledgements
The experiments were run on the servers of the Future SOC Lab, Hasso Plattner Institute in Potsdam. This work is part of the research project “Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.
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Sidorova, J., Rosander, O., Skold, L., Grahn, H., Lundberg, L. (2019). Finding a Healthy Equilibrium of Geo-demographic Segments for a Telecom Business: Who Are Malicious Hot-Spotters?. In: Tsihrintzis, G., Sotiropoulos, D., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 149 . Springer, Cham. https://doi.org/10.1007/978-3-319-94030-4_8
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