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Advanced Analytics of Large Connected Data Based on Similarity Modeling

  • Tomáš Skopal
  • Ladislav PeškaEmail author
  • Irena Holubová
  • Petr Paščenko
  • Jan Hučín
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)

Abstract

Collecting various types of data about users/clients in order to improve the services and competitiveness of companies has a long history. However, these approaches are often based on classical statistical methods and an assumption of limited computational power. In this paper we introduce the vision of our applied research project targeting to the financial sector. Our main goal is to develop an automated software solution for similarity modeling over big and semi-structured graph data representing behavior of bank clients. The main aim of similarity models is to improve the decision process in risk management, marketing, security and related areas.

Keywords

Similarity modeling Big Data Analysis of graph data Transactional data Linked data 

Notes

Acknowledgments

This work was supported in part by the Technology Agency of the Czech Republic (TAČR) project no. TH03010276 and by Czech Science Foundation (GAČR) project no. 17-22224S.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tomáš Skopal
    • 1
  • Ladislav Peška
    • 1
    Email author
  • Irena Holubová
    • 1
  • Petr Paščenko
    • 2
  • Jan Hučín
    • 2
  1. 1.Department of Software Engineering, Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic
  2. 2.Profinit EU, s.r.o.PragueCzech Republic

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