Interactive Visualization Interfaces for Big Data Analysis Using Combination of Dimensionality Reduction Methods: A Brief Review

  • Ana C. Umaquinga-CriolloEmail author
  • Diego H. Peluffo-Ordóñez
  • Paúl D. Rosero-Montalvo
  • Pamela E. Godoy-Trujillo
  • Henry Benítez-Pereira
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1110)


The Big Data analysis allows to generate knowledge based on mathematical models that surpass human capabilities, and therefore it is necessary to have robust computer systems. In this connection, the dimensionality reduction (DR) allows to perform approximations to make data perceptible in a simple and compact way while also the computational cost is reduced. Additionally, interactive interfaces enable the user to work with algorithms involving complex mathematical and statistical processes typically aimed at providing weighting factors to each RD algorithm to find the best way to represent data at a low dimension. In this study, a bibliographic re-view of the different models of interactive interfaces for the analysis of Big Data using RD is presented, by considering different, existing proposals and approaches on how to display the information. Particularly, those approaches based on mental processes and uses of color along with an intuitive handling are of special interest.


Big data Business intelligence Data mining Dimensionality reduction Interactive interface 



The authors thank the SDAS Research Group ( and “Universidad Técnica del Norte”.


  1. 1.
    Beshers, C.G., Feiner, S.K.: Automated design of data visualizations. Sci. Vis.-Adv. Appl. Rosemblum Al Eds, pp. 88–102 (1994)Google Scholar
  2. 2.
    Peluffo-Ordónez, D.H., Lee, J.A., Verleysen, M.: Short review of dimensionality reduction methods based on stochastic neighbour embedding. In: Advances in Self-Organizing Maps and Learning Vector Quantization, pp. 65–74. Springer (2014)Google Scholar
  3. 3.
    Borg, I., Groenen, P.J.: Modern Multidimensional Scaling: Theory and Applications. Springer Science & Business Media (2005)Google Scholar
  4. 4.
    Dai, W., Hu, P.: Research on personalized behaviors recommendation system based on cloud computing. TELKOMNIKA Indones. J. Electr. Eng. 12, 1480–1486 (2013)Google Scholar
  5. 5.
    Geppert, H., Vogt, M., Bajorath, J.: Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model. 50(2), 205–216 (2010)CrossRefGoogle Scholar
  6. 6.
    Riquelme Santos, J.C., Ruiz, R., Gilbert, K.: Minería de datos: Conceptos y tendencias. Intel. Artif. Rev. Iberoam. Intel. Artif. 10(29), 11–18 (2006)Google Scholar
  7. 7.
    Cleveland, W.S.: Visualizing Data. Hobart Press (1993)Google Scholar
  8. 8.
    Inselberg, A., Dimsdale, B.: Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: Proceedings of the 1st Conference on Visualization 1990, pp. 361–378 (1990)Google Scholar
  9. 9.
    Vallejos, S.J.: Minería de Datos. Universidad Nacional del Nordeste, Corrientes, Argentina (2006)Google Scholar
  10. 10.
    Keim, D.A.: Visual techniques for exploring databases (1997)Google Scholar
  11. 11.
    Keim, D.A., Kriegel, H.-P.: Visualization techniques for mining large databases: a comparison. IEEE Trans. Knowl. Data Eng. 8(6), 923–938 (1996)CrossRefGoogle Scholar
  12. 12.
    Keim, D.A., Mansmann, F., Schneidewind, J., Ziegler, H.: Challenges in visual data analysis. In: Tenth International Conference on Information Visualization, IV 2006, pp. 9–16 (2006)Google Scholar
  13. 13.
    Alvarado-Pérez, J.C., Bolaños-Ramírez, H., Peluffo-Ordóñez, D.H., Murillo, S.: Knowledge discovery in databases from a perspective of intelligent information visualization. In: 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA), pp. 1–7 (2015)Google Scholar
  14. 14.
    Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1(2), 69–91 (1985)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Ahlberg, C., Wistrand, E.: IVEE: an information visualization and exploration environment. In: Proceedings of Visualization 1995 Conference, pp. 66–73 (1995)Google Scholar
  16. 16.
    Kerren, A., Ebert, A., Meyer, J.: Human-Centered Visualization Environments, GI-Dagstuhl Research Seminar, Dagstuhl Castle, Germany, March 5–8, 2006. Revised Lectures. Lecture Notes in Computer Science, vol. 4417 (2007)Google Scholar
  17. 17.
    López C. P.: Minería de datos: técnicas y herramientas. Editorial Paraninfo, 2007Google Scholar
  18. 18.
    Tascón, M.: Pasado, presente y futuro. Big Data 95, 47 (2013)Google Scholar
  19. 19.
    Pimentel, D., Cataldi, M., Muñiz, G.: De la Visualización a la Sensorización de Información. Blucher Des. Proc. 1(7), 129–133 (2013)Google Scholar
  20. 20.
    Umaquinga-Criollo, A.C., Peluffo-Ordónez, D.H., Cabrera- Álvarez, M.V., Alvarado-Pérez, J.C., Anaya-Isaza, A.J.: Propuesta de análisis visual de datos en Big Data usando reducción de dimensión interactiva. Tecnol. Apl. Ing. FICA-UTN (2016)Google Scholar
  21. 21.
    Peluffo-Ordóñez, D.H., Lee, J.A., Verleysen, M.: Generalized kernel framework for unsupervised spectral methods of dimensionality reduction. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 171–177 (2014)Google Scholar
  22. 22.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)CrossRefGoogle Scholar
  23. 23.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  24. 24.
    Hinton, G., Roweis, S.: Stochastic neighbor embedding. In: NIPS, vol. 15, pp. 833–840 (2002)Google Scholar
  25. 25.
    Harmann, J., Murphy, M.P., Peters, C.S., Staecker, P.C.: Homotopy equivalence in graph-like digital topological spaces. ArXiv 14082584 (2014)Google Scholar
  26. 26.
    Peluffo-Ordónez, D.H., Alvarado-Pérez, J.C., Lee, J.A., Verleysen, M.: Geometrical homotopy for data visualization. In: European Symposium on Artificial Neural Networks (ESANN 2015). Computational Intelligence and Machine Learning (2015)Google Scholar
  27. 27.
    Peña-ünigarro, D.F., et al.: Interactive visualization methodology of high-dimensional data with a color-based model for dimensionality reduction. In: 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), pp. 1–7 (2016)Google Scholar
  28. 28.
    Rosero-Montalvo, P.D., Peña-Unigarro, D.F., Peluffo, D.H., Castro-Silva, J.A., Umaquinga, A., Rosero-Rosero, E.A.: Data visualization using interactive dimensionality reduction and improved color-based interaction model. In: Biomedical Applications Based on Natural and Artificial Computing, pp. 289–298 (2017)Google Scholar
  29. 29.
    Salazar-Castro, J.A., et al.: Dimensionality reduction for interactive data visualization via a Geo-Desic approach. In: 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1–6 (2016)Google Scholar
  30. 30.
    Rosero-Montalvo, P., et al.: Interactive data visualization using dimensionality reduction and similarity-based representations. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 334–342 (2016)Google Scholar
  31. 31.
    Salazar-Castro, J.A., Rosas-Narváez, Y.C., Pantoja, A.D., Alvarado-Pérez, J.C., Peluffo-Ordóñez, D.H.: Interactive interface for efficient data visualization via a geometric approach. In: 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA), pp. 1–6 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ana C. Umaquinga-Criollo
    • 1
    Email author
  • Diego H. Peluffo-Ordóñez
    • 1
    • 2
  • Paúl D. Rosero-Montalvo
    • 1
  • Pamela E. Godoy-Trujillo
    • 1
  • Henry Benítez-Pereira
    • 3
  1. 1.Universidad Técnica del NorteIbarraEcuador
  2. 2.SDAS Research Group ( Yachay TechUrcuquíEcuador
  3. 3.Instituto Superior Tecnológico Superior 17 de JulioUrcuquiEcuador

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