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Finding a healthy equilibrium of geo-demographic segments for a telecom business: Who are malicious hot-spotters?
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
Telenor, SWE.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0001-9947-1088
Vise andre og tillknytning
2019 (engelsk)Inngår i: 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, s. 187-196Kapittel i bok, del av antologi (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer Science and Business Media Deutschland GmbH , 2019. s. 187-196
Serie
Intelligent Systems Reference Library, ISSN 1868-4394 ; 149
Emneord [en]
Business intelligence, Combinatorial optimization, Fuzzy logic, Geo-demographic segments, Mobility data, MOSAIC
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-16885DOI: 10.1007/978-3-319-94030-4_8Scopus ID: 2-s2.0-85049522294ISBN: 978-3-319-94029-8 (tryckt)OAI: oai:DiVA.org:bth-16885DiVA, id: diva2:1239961
Tilgjengelig fra: 2018-08-20 Laget: 2018-08-20 Sist oppdatert: 2019-10-21bibliografisk kontrollert

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