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  • 1.
    Boeva, Veselka
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Angelova, Milena
    Technical University Sofia, BUL.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Rosander, Oliver
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tsiporkova, Elena
    Collective Center for the Belgian Technological Industry, BEL.
    Evolutionary clustering techniques for expertise mining scenarios2018In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2 / [ed] van den Herik J.,Rocha A.P., SciTePress , 2018, Vol. 2, p. 523-530Conference paper (Refereed)
    Abstract [en]

    The problem addressed in this article concerns the development of evolutionary clustering techniques that can be applied to adapt the existing clustering solution to a clustering of newly collected data elements. We are interested in clustering approaches that are specially suited for adapting clustering solutions in the expertise retrieval domain. This interest is inspired by practical applications such as expertise retrieval systems where the information available in the system database is periodically updated by extracting new data. The experts available in the system database are usually partitioned into a number of disjoint subject categories. It is becoming impractical to re-cluster this large volume of available information. Therefore, the objective is to update the existing expert partitioning by the clustering produced on the newly extracted experts. Three different evolutionary clustering techniques are considered to be suitable for this scenario. The proposed techniques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

  • 2.
    Podapati, Sasidahr
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sköld, L.
    Telenor, SWE.
    Rosander, Oliver
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sidorova, Yulia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Fuzzy recommendations in marketing campaigns2017In: Communications in Computer and Information Science / [ed] Darmont J.,Kirikova M.,Norvag K.,Wrembel R.,Papadopoulos G.A.,Gamper J.,Rizzi S., 2017, Vol. 767, p. 246-256Conference paper (Refereed)
    Abstract [en]

    The population in Sweden is growing rapidly due to immigration. In this light, the issue of infrastructure upgrades to provide telecommunication services is of importance. New antennas can be installed at hot spots of user demand, which will require an investment, and/or the clientele expansion can be carried out in a planned manner to promote the exploitation of the infrastructure in the less loaded geographical zones. In this paper, we explore the second alternative. Informally speaking, the term Infrastructure-Stressing describes a user who stays in the zones of high demand, which are prone to produce service failures, if further loaded. We have studied the Infrastructure-Stressing population in the light of their correlation with geo-demographic segments. This is motivated by the fact that specific geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic is applied to create an interface between big data, numeric methods for its processing, and a manager who wants a comprehensible summary. © 2017, Springer International Publishing AG.

  • 3.
    Sidorova, Yulia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Rosander, Oliver
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Skold, Lars
    telenor, SWE.
    Recommendations for marketing campaigns in telecommunication business based on the footprint analysis: Who is a good client?2017In: 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 513-518Conference paper (Refereed)
    Abstract [en]

    A major investment made by a telecom operator goes into the infrastructure and its maintenance, while business revenues depend on how efficiently it is exploited. We present a data-driven analytic strategy based on combinatorial optimization and analysis of historical data. The data cover historical mobility in one region of Sweden during a week. Applying the proposed method in a case study, we have identified the optimal combination of geodemographic segments in the customer base, developed a functionality to assess the potential of a planned marketing campaign, and investigated how many and which segments to target for customer base growth. A comprehensible summary of the conclusions is created via execution of the queries with a fuzzy logic component. © 2017 IEEE.

  • 4.
    Sidorova, Yulia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Rosander, Oliver
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sköld, Lars
    Telenor, SWE.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Finding a healthy equilibrium of geo-demographic segments for a telecom business: Who are malicious hot-spotters?2019In: 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.

  • 5.
    Sidorova, Yulia
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sköld, Lars
    Telenor Sverige AB, SWE.
    Rosander, Oliver
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Optimizing utilization in cellular radio networks using mobility data2019In: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, Vol. 20, no 1, p. 37-64Article in journal (Refereed)
    Abstract [en]

    The main resource for any telecom operator is the physical radio cell network. We present two related methods for optimizing utilization in radio networks: Tetris optimization and selective cell expansion. Tetris optimization tries to find the mix of users from different market segments that provides the most even load in the network. Selective cell expansion identifies hotspot cells, expands the capacity of these radio cells, and calculates how many subscribers the radio network can handle after the expansions. Both methods are based on linear programming and use mobility data, i.e., data defining where different categories of subscribers tend to be during different times of the week. Based on real-world mobility data from a region in Sweden, we show that Tetris optimization based on six user segments made it possible to increase the number of subscribers by 58% without upgrading the physical infrastructure. The same data show that by selectively expanding less than 6% of the cells we are able to increase the number of subscribers by more than a factor of three without overloading the network. We also investigate the best way to combine Tetris optimization and selective cell expansion. © 2018 The Author(s)

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