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Advanced Behavioral Analyses Using Inferred Social Networks: A Vision

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Database and Expert Systems Applications (DEXA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1062))

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Abstract

The success of many businesses is based on a thorough knowledge of their clients. There exists a number of supervised as well as unsupervised data mining or other approaches that allow to analyze data about clients, their behavior or environment. In our ongoing project focusing primarily on bank clients, we propose an innovative strategy that will overcome shortcomings of the existing methods. From a given set of user activities, we infer their social network in order to analyze user relationships and behavior. For this purpose, not just the traditional direct facts are incorporated, but also relationships inferred using similarity measures and statistical approaches, with both possibly limited measures of reliability and validity in time. Such networks would enable analyses of client characteristics from a new perspective and could provide otherwise impossible insights. However, there are several research and technical challenges making the outlined pursuit novel, complex and challenging as we outline in this vision paper.

This work was supported in part by the Technology Agency of the Czech Republic (TAČR) project number TH03010276 and by Czech Science Foundation (GAČR) project number 19-01641S.

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Notes

  1. 1.

    Instead of fully materialized matrices approximations can be used.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic

    Irena Holubová, Martin Svoboda, Tomáš Skopal, David Bernhauer & Ladislav Peška

  2. Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic

    David Bernhauer

Authors
  1. Irena Holubová
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  2. Martin Svoboda
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  3. Tomáš Skopal
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  4. David Bernhauer
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  5. Ladislav Peška
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Corresponding author

Correspondence to Martin Svoboda .

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Editors and Affiliations

  1. Institute of Telecooperation, Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Anderst-Kotsis

  2. Software Competence Center Hagenberg, Hagenberg, Austria

    A Min Tjoa

  3. Institute of Telecooperation, Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Ismail Khalil

  4. ENSIT, LaTICE, University of Tunis, Tunis, Tunisia

    Mourad Elloumi

  5. Software Competence Center, Hagenberg, Austria

    Atif Mashkoor

  6. Steyregg, Oberösterreich, Austria

    Johannes Sametinger

  7. Edificio 204, ICT Division,TECNALIA, Derio, Vizcaya, Spain

    Xabier Larrucea

  8. Top 74, Innsbruck, Tirol, Austria

    Anna Fensel

  9. Hagenberg Gmbh, Software Competence Center, Hagenberg im Mühlkreis, Oberösterreich, Austria

    Jorge Martinez-Gil

  10. Software Competence Center Hagenberg, SC, Hagenberg im Mühlkreis, Oberösterreich, Austria

    Bernhard Moser

  11. University of Twente, ENSCHEDE, Overijssel, The Netherlands

    Christin Seifert

  12. Bauhaus Universität Weimar, Weimar, Thüringen, Germany

    Benno Stein

  13. MiCS, Media Computer Science, University of Passau, Passau, Bayern, Germany

    Michael Granitzer

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Holubová, I., Svoboda, M., Skopal, T., Bernhauer, D., Peška, L. (2019). Advanced Behavioral Analyses Using Inferred Social Networks: A Vision. In: Anderst-Kotsis, G., et al. Database and Expert Systems Applications. DEXA 2019. Communications in Computer and Information Science, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-27684-3_26

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  • DOI: https://doi.org/10.1007/978-3-030-27684-3_26

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