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Finding a healthy equilibrium of geo-demographic segments for a telecom business: Who are malicious hot-spotters?
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-1024-168X
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Telenor, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0001-9947-1088
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2019 (English)In: Machine Learning Paradigms: Advances in Data Analytics / [ed] George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain, Springer Science and Business Media Deutschland GmbH , 2019, p. 187-196Chapter in book (Refereed)
Abstract [en]

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. © 2019, Springer International Publishing AG, part of Springer Nature.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2019. p. 187-196
Series
Intelligent Systems Reference Library, ISSN 1868-4394 ; 149
Keywords [en]
Business intelligence, Combinatorial optimization, Fuzzy logic, Geo-demographic segments, Mobility data, MOSAIC
National Category
Telecommunications Business Administration Computer Sciences
Identifiers
URN: urn:nbn:se:bth-16885DOI: 10.1007/978-3-319-94030-4_8Scopus ID: 2-s2.0-85049522294ISBN: 978-3-319-94029-8 (print)OAI: oai:DiVA.org:bth-16885DiVA, id: diva2:1239961
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge FoundationAvailable from: 2018-08-20 Created: 2018-08-20 Last updated: 2021-12-09Bibliographically approved

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Sidorova, YuliaGrahn, HåkanLundberg, Lars

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