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)


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.


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



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.


  1. 1.
    Byeon, W., Breuel, T.M., Raue, F., Liwicki, M.: Scene labeling with LSTM recurrent neural networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3547–3555, June 2015Google Scholar
  2. 2.
    Camerra, A., Shieh, J., Palpanas, T., Rakthanmanon, T., Keogh, E.: Beyond one billion time series: indexing and mining very large time series collections with \(i\)SAX2+. Knowl. Inf. Syst. 39(1), 123–151 (2014)CrossRefGoogle Scholar
  3. 3.
    Cao, D., Liu, J.: Research on dynamic time warping multivariate time series similarity matching based on shape feature and inclination angle. J. Cloud Comput. 5(1), 11 (2016)CrossRefGoogle Scholar
  4. 4.
    Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endow. 1(2), 1542–1552 (2008)CrossRefGoogle Scholar
  5. 5.
    Dong, Y., Chawla, N.V., Swami, A.: Metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, New York, NY, USA, pp. 135–144. ACM (2017)Google Scholar
  6. 6.
    Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, New York, NY, USA, pp. 855–864. ACM (2016)Google Scholar
  7. 7.
    Hu, Q., Xie, S., Zhang, J., Zhu, Q., Guo, S., Yu, P.S.: Heterosales: utilizing heterogeneous social networks to identify the next enterprise customer. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Republic and Canton of Geneva, Switzerland, pp. 41–50. International World Wide Web Conferences Steering Committee (2016)Google Scholar
  8. 8.
    Liu, Q., Xiang, B., Chen, E., Xiong, H., Tang, F., Yu, J.X.: Influence maximization over large-scale social networks: a bounded linear approach. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, New York, NY, USA, pp. 171–180. ACM (2014)Google Scholar
  9. 9.
    Wang, J., Zhou, S., Guan, J.: Detecting potential collusive cliques in futures markets based on trading behaviors from real data. Neurocomputing 92, 44–53 (2012)CrossRefGoogle Scholar
  10. 10.
    Zhang, J., Cui, L., Yu, P.S., Lv, Y.: BL-ECD: broad learning based enterprise community detection via hierarchical structure fusion. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, New York, NY, USA, pp. 859–868. ACM (2017)Google Scholar

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

Personalised recommendations