Skip to main content
SpringerLink
Log in

Interactive Information Visualization Models: A Systematic Literature Review

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2023 (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13956 ))

Included in the following conference series:

Abstract

Interactive information visualization models aim to make dimensionality reduction (DR) accessible to non-expert users through interactive visualization frameworks. This systematic literature review explores the role of DR and information visualization (IV) techniques in interactive models (IM). We search relevant bibliographic databases, including IEEE Xplore, Springer Link, and Web of Science, for publications from the last five years. We identify 1448 scientific articles, which we then narrow down to 52 after screening and selection. This study addresses three research questions, revealing that the number of articles focused on interactive DR-oriented models has been in the minority in the last five years. However, related topics such as IV techniques or RD methods have increased. Trends are identified in the development of interactive models, as well as in IV techniques and RD methods. For example, researchers are increasingly proposing new DR methods or modifying existing ones rather than relying solely on established techniques. Furthermore, scatter plots have emerged as the predominant option for IV in interactive models, with limited options for customizing the display of raw data and details in application windows. Overall, this review provides insights into the current state of interactive IV models for DR and highlights areas for further research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Umaquinga-Criollo, A.C., Peluffo-Ordóñez, D.H., Rosero-Montalvo, P.D., Godoy-Trujillo, P.E., Benítez-Pereira, H.: Interactive visualization interfaces for big data analysis using combination of dimensionality reduction methods: a brief review. In: Basantes-Andrade, A., Naranjo-Toro, M., Zambrano Vizuete, M., Botto-Tobar, M. (eds.) Technology, Sustainability and Educational Innovation (TSIE). TSIE 2019. Advances in Intelligent Systems and Computing, vol 1110. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37221-7-17

  2. Ortega-Bustamante, M.C., et al.: Introducing the concept of interaction model for interactive dimensionality reduction and data visualization. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12250, pp. 193–203. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58802-1_14

    Chapter  Google Scholar 

  3. Peluffo-Ordóñez, D.H., et al.: Geometrical homotopy for data visualization. In: ESANN (2015). http://hdl.handle.net/2078.1/168996

  4. Lee, J.A., et al.: Multi-scale similarities in stochastic neighbour embedding: reducing dimensionality while preserving both local and global structure. Neurocomputing 169, 246–261 (2015). https://doi.org/10.1016/j.neucom.2014.12.095

    Article  Google Scholar 

  5. Steed, C.A.: Interactive data visualization. In: Data Analytics for Intelligent Transportation Systems, pp. 165–190. Elsevier Inc. (2017). https://doi.org/10.1016/B978-0-12-809715-1.00007-9

  6. Ayesha, S., Hanif, M.K., Talib, R.: Overview and comparative study of dimensionality reduction techniques for high dimensional data. Inf. Fus. 59, 44–58 (2020). https://doi.org/10.1016/j.inffus.2020.01.005

    Article  Google Scholar 

  7. Kitchenham, B., Charters, S.: Guidelines for performing Systematic Literature Reviews in Software Engineering (2007). https://userpages.uni-koblenz.de/~laemmel/esecourse/slides/slr.pdf. Accessed 07 Dec 2021

  8. Fernandez, A., Insfran, E., Abrahão, S.: Usability evaluation methods for the web: A systematic mapping study. Inf. Softw. Technol. 53(8), 789–817 (2011). https://doi.org/10.1016/J.INFSOF.2011.02.007

  9. Genero Bocco, M., Cruz Lemus, J.A., Piattini Velthuis, M.: Métodos de Investigación en Ingeniería del Software. http://190.57.147.202:90/xmlui/handle/123456789/2525. Accessed 07 Dec 2021

  10. Institute of Electrical and Electronic Engineers: Institute of Electrical and Electronic Engineers (2021). https://ieeexplore.ieee.org/Xplore/home.jsp. Accessed 08 Dec 2021

  11. Springer Link: Springer Link (2021). https://link.springer.com/. Accessed 08 Dec 2021

  12. Web of Science: Web of Science Core Collection (2021). https://www.webofscience.com/wos/woscc/basic-search. Accessed 08 Dec 2021

  13. Salazar-Castro, J.A., et al.: A novel color-based data visualization approach using a circular interaction model and dimensionality reduction. In: Huang, T., Lv, J., Sun, C., Tuzikov, A.V. (eds.) ISNN 2018. LNCS, vol. 10878, pp. 557–567. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92537-0_64

    Chapter  Google Scholar 

  14. Basante-Villota, C.K., et al.: Angle-based model for interactive dimensionality reduction and data visualization. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds.) IWAIPR 2018. LNCS, vol. 11047, pp. 149–157. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01132-1_17

    Chapter  Google Scholar 

  15. 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), November 2016, pp. 1–6 (2016). https://doi.org/10.1109/LA-CCI.2016.7885740

  16. Pena-Unigarro, D.F., et al.: Interactive visualization methodology of high-dimensional data with a color-based model for dimensionality reduction. In: 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016, pp. 1–7 (2016). https://doi.org/10.1109/STSIVA.2016.7743318

  17. 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: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2017. LNCS, vol. 10338, pp. 289–298. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59773-7_30

    Chapter  Google Scholar 

  18. Rosero-Montalvo, P., et al.: Interactive data visualization using dimensionality reduction and similarity-based representations. In: Beltrán-Castañón, C., Nyström, I., Famili, F. (eds.) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2016. Lecture Notes in Computer Science, vol. 10125. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52277-7-41

  19. Vélez-Falconí, M., González-Vergara, J., Peluffo-Ordóñez, D.H.: Inverse data visualization framework (IDVF): towards a prior-knowledge-driven data visualization. In: Florez, H., Misra, S. (eds.) ICAI 2020. CCIS, vol. 1277, pp. 266–280. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61702-8_19

    Chapter  Google Scholar 

  20. Sacha, D., et al.: Visual interaction with dimensionality reduction: a structured literature analysis. IEEE Trans. Vis. Comput. Graph 23(1), 241–250 (2017). https://doi.org/10.1109/TVCG.2016.2598495

    Article  MathSciNet  Google Scholar 

  21. Sacha, D., et al.: What you see is what you can change: human-centered machine learning by interactive visualization. Neurocomputing 268, 164–175 (2017). https://doi.org/10.1016/j.neucom.2017.01.105

    Article  Google Scholar 

  22. Turkay, C., Kaya, E., Balcisoy, S., Hauser, H.: Designing progressive and interactive analytics processes for high-dimensional data analysis. IEEE Trans. Vis. Comput. Graph 23(1), 131–140 (2017). https://doi.org/10.1109/TVCG.2016.2598470

    Article  Google Scholar 

  23. Rosasn-Arias, L., Sanchezn-Perez, G., Toscano-Medina, L.K., Perez-Meana, H.M., Portillo-Portillo, J.: A graphical user interface for fast evaluation and testing of machine learning models performance. In: 2019 7th International Workshop on Biometrics and Forensics (IWBF), May 2019, pp. 1–5 (2019). https://doi.org/10.1109/IWBF.2019.8739238

  24. Fujiwara, T., Kwon, O.H., Ma, K.L.: Supporting analysis of dimensionality reduction results with contrastive learning. IEEE Trans. Vis. Comput. Graph. 26(1), 45–55 (2020). https://doi.org/10.1109/TVCG.2019.2934251

    Article  Google Scholar 

  25. Liutvinavičiene, J., Kurasova, O.: Multi-level massive data visualization: methodology and use cases. Baltic J. Mod. Comput. 6(4) (2018). https://doi.org/10.22364/bjmc.2018.6.4.01

  26. Basante-Villota, C.K., Ortega-Castillo, C.M., Peña-Unigarro, D.F., Revelo-Fuelagán, J.E., Salazar-Castro, J.A., Peluffo-Ordóñez, D.H.: Comparative analysis between embedded-spaces-based and kernel-based approaches for interactive data representation. In: Serrano C., J.E., Martínez-Santos, J.C. (eds.) CCC 2018. CCIS, vol. 885, pp. 28–38. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98998-3_3

    Chapter  Google Scholar 

  27. Salazar-Castro, J.A., et al.: Generalized low-computational cost Laplacian eigenmaps. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science, vol. 11314, pp. 661–669. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03493-1_69

  28. Ortega-Bustamante, M.C., Hasperué, W., Peluffo-Ordóñez, D.H., González-Vergara, J., Marín-Gaviño, J., Velez-Falconi, M.: Generalized spectral dimensionality reduction based on kernel representations and principal component analysis. In: Gervasi, O., et al. (eds.) ICCSA 2021. LNCS, vol. 12952, pp. 512–523. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86973-1_36

    Chapter  Google Scholar 

  29. Peña-Unigarro, D.F., et al.: Interactive data visualization using dimensionality reduction and dissimilarity-based representations. In: Yin, H., et al. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 461–469. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68935-7_50

    Chapter  Google Scholar 

  30. Castermans, T., et al.: SolarView: low distortion radial embedding with a focus. IEEE Trans. Vis. Comput. Graph. 25(10), 2969–2982 (2019). https://doi.org/10.1109/TVCG.2018.2865361

    Article  Google Scholar 

  31. Sanchez, A., Soguero-Ruiz, C., Mora-Jiménez, I., Rivas-Flores, F.J., Lehmann, D.J., Rubio-Sánchez, M.: Scaled radial axes for interactive visual feature selection: a case study for analyzing chronic conditions. Exp. Syst. Appl. 100, 182–196 (2018). https://doi.org/10.1016/j.eswa.2018.01.054

  32. Becker, M., Lippel, J., Zielke, T.: Dimensionality reduction for data visualization and linear classification, and the trade-off between robustness and classification accuracy. In: Proceedings of the International Conference on Pattern Recognition, pp. 6478–6485 (2020). https://doi.org/10.1109/ICPR48806.2021.9412865

  33. Wagner Filho, J.A., Rey, M.F., Freitas, C.M.D.S., Nedel, L.: Immersive visualization of abstract information: an evaluation on dimensionally-reduced data scatterplots. In: 25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings, August 2018, pp. 483–490 (2018). https://doi.org/10.1109/VR.2018.8447558

  34. Zhou, Z., Zhang, X., Zhou, X., Liu, Y.: Semantic-aware visual abstraction of large-scale social media data with geo-tags. IEEE Access 7, 114851–114861 (2019). https://doi.org/10.1109/ACCESS.2019.2935471

    Article  Google Scholar 

  35. Krause, J., Dasgupta, A., Fekete, J.-D., Bertini, E.: SeekAView: an intelligent dimensionality reduction strategy for navigating high-dimensional data spaces. In: 2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV), October 2016, vol. 35, no. 11, pp. 11–19 (2016). https://doi.org/10.1109/LDAV.2016.7874305

  36. Marcilio-Jr, W.E., Eler, D.M.: SADIRE: a context-preserving sampling technique for dimensionality reduction visualizations. J. Vis. 23(6), 999–1013 (2020). https://doi.org/10.1007/s12650-020-00685-4

    Article  Google Scholar 

  37. Kammer, D., et al.: Glyphboard: visual exploration of high-dimensional data combining glyphs with dimensionality reduction. IEEE Trans. Vis. Comput. Graph. 26(4), 1661–1671 (2020). https://doi.org/10.1109/TVCG.2020.2969060

    Article  Google Scholar 

  38. Marcílio-Jr, W.E., Eler, D.M.: Explaining dimensionality reduction results using Shapley values. Exp. Syst. Appl. 178, 115020 (2021). https://doi.org/10.1016/j.eswa.2021.115020

    Article  Google Scholar 

  39. Nguyen, B.D.Q., Hewett, R., Dang, T.: NetScatter: visual analytics of multivariate time series with a hybrid of dynamic and static variable relationships. In: 2021 IEEE 14th Pacific Visualization Symposium (PacificVis), April 2021, vol. 2021-April, pp. 52–60 (2021). https://doi.org/10.1109/PacificVis52677.2021.00015

  40. Höllt, T., Vilanova, A., Pezzotti, N., Lelieveldt, B.P.F., Hauser, H.: Focus+context exploration of hierarchical embeddings. Comput. Graph. Forum 38(3), 569–579 (2019). https://doi.org/10.1111/cgf.13711

    Article  Google Scholar 

  41. Wang, Y.C., Zhang, Q., Lin, F., Goh, C.K., Seah, H.S.: PolarViz: a discriminating visualization and visual analytics tool for high-dimensional data. Vis. Comput. 35(11), 1567–1582 (2018). https://doi.org/10.1007/s00371-018-1558-y

    Article  Google Scholar 

  42. Marcílio-Jr, W.E., Eler, D.M., Paulovich, F.v., Rodrigues-Jr, J.F., Artero, A.O.: ExplorerTree: a focus+context exploration approach for 2d embeddings. Big Data Res. 25, 100239 (2021). https://doi.org/10.1016/j.bdr.2021.100239

  43. Garrison, L., Muller, J., Schreiber, S., Oeltze-Jafra, S., Hauser, H., Bruckner, S.: DimLift: interactive hierarchical data exploration through dimensional bundling. IEEE Trans. Vis. Comput. Graph. 27(6), 2908–2922 (2021). https://doi.org/10.1109/TVCG.2021.3057519

    Article  Google Scholar 

  44. Matute, J., Linsen, L.: Hinted star coordinates for mixed data. Comput. Graph. Forum 39, 117–133 (2019). https://doi.org/10.1111/cgf.13666

    Article  Google Scholar 

  45. Sanchez, A., Raya, L., Mohedano-Munoz, M.A., Rubio-Sánchez, M.: Feature selection based on star coordinates plots associated with eigenvalue problems. Vis. Comput. 37(2), 203–216 (2020). https://doi.org/10.1007/s00371-020-01793-w

    Article  Google Scholar 

  46. Roma, G., Xambó, A., Green, O., Tremblay, P.A.: A general framework for visualization of sound collections in musical interfaces. Appl. Sci. (Switzerland) 11(24) (2021). https://doi.org/10.3390/app112411926

  47. Baker, F.N., Porollo, A.: CoeViz: a web-based integrative platform for interactive visualization of large similarity and distance matrices. Data (Basel) 3(1), 4 (2018). https://doi.org/10.3390/data3010004

  48. Pondel, M., Korczak, J.: Recommendations based on collective intelligence – case of customer segmentation. In: Ziemba, E. (ed.) AITM/ISM -2018. LNBIP, vol. 346, pp. 73–92. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15154-6_5

    Chapter  Google Scholar 

  49. Chen, X., Zeng, G., Kosinka, J., Telea, A.: Scalable visual exploration of 3d shape databases via feature synthesis and selection. In: Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2020. Communications in Computer and Information Science, vol. 1474, pp. 153–182. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-94893-1_7

  50. Sohns, J.-T., Schmitt, M., Jirasek, F., Hasse, H., Leitte, H.: Attribute-based explanations of non-linear embeddings of high-dimensional data, July 2021. http://arxiv.org/abs/2108.08706

  51. Mazumdar, D., Neto, M.P., Paulovich, F.v.: Random forest similarity maps: a scalable visual representation for global and local interpretation. Electronics (Switzerland) 10(22), 2862 (2021). https://doi.org/10.3390/electronics10222862

  52. Urpa, L.M., Anders, S.: Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data. BMC Bioinform. 20(1), 221 (2019). https://doi.org/10.1186/s12859-019-2780-y

  53. Fujiwara, T., Wei, X., Zhao, J., Ma, K.L.: Interactive dimensionality reduction for comparative analysis. IEEE Trans. Vis. Comput. Graph. 28(1), 758–768 (2022). https://doi.org/10.1109/TVCG.2021.3114807

    Article  Google Scholar 

  54. Schaefer, G.: Interactive browsing of large image repositories. In: Patnaik, S., Jain, V. (eds.) Recent Developments in Intelligent Computing, Communication and Devices. AISC, vol. 752, pp. 1–7. Springer, Singapore (2019). https://doi.org/10.1007/978-981-10-8944-2_1

    Chapter  Google Scholar 

  55. Li, Z., Zhang, C., Zhang, Y., Zhang, J.: SemanticAxis: exploring multi-attribute data by semantic construction and ranking analysis. J. Vis. 24(5), 1065–1081 (2021). https://doi.org/10.1007/s12650-020-00733-z

    Article  Google Scholar 

  56. Wang, Z., Ferreira, N., Wei, Y., Bhaskar, A.S., Scheidegger, C.: Gaussian cubes: real-time modeling for visual exploration of large multidimensional datasets. IEEE Trans. Vis. Comput. Graph. 23(1), 681–690 (2017). https://doi.org/10.1109/TVCG.2016.2598694

    Article  Google Scholar 

  57. Yu, H., Li, S.: Improved interactive color visualization approach for hyperspectral images. Inf. Vis. 21(2), 153–165 (2022). https://doi.org/10.1177/14738716211048142

  58. Feng, K., Wang, Y., Zhao, Y., Fu, C.W., Cheng, Z., Chen, B.: Cluster aware Star Coordinates. J. Vis. Lang. Comput. 44, 28–38 (2018). https://doi.org/10.1016/j.jvlc.2017.11.003

    Article  Google Scholar 

  59. Sabando, M.V., et al.: ChemVA: interactive visual analysis of chemical compound similarity in virtual screening August 2020. http://arxiv.org/abs/2008.13150

  60. Jäckle, D., Fischer, F., Schreck, T., Keim, D.A.: Temporal MDS plots for analysis of multivariate data. IEEE Trans. Vis. Comput. Graph. 22(1), 141–150 (2016). https://doi.org/10.1109/TVCG.2015.2467553

    Article  Google Scholar 

Download references

Acknowledgements

Authors acknowledge the valuable support by SDAS Research Group (https://sdas-group.com/, accessed on 26 Mars 2023).

Author information

Authors and Affiliations

  1. Facultad de Ingeniería en Ciencias Aplicadas, Universidad Técnica del Norte, Ibarra, Ecuador

    MacArthur Ortega-Bustamante & Daisy Imbaquingo

  2. III-LIDI, Facultad de Informática, Universidad Nacional de la Plata, La Plata, Argentina

    MacArthur Ortega-Bustamante, Waldo Hasperué & Daisy Imbaquingo

  3. College of Computing, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Diego H. Peluffo-Ordóñez, Hind Raki, Yahya Aalaila & Mouad Elhamdi

  4. SDAS Research Group, 43150, Ben Guerir, Morocco

    Diego H. Peluffo-Ordóñez, Hind Raki, Yahya Aalaila, Mouad Elhamdi & Lorena Guachi-Guachi

  5. Department of Mechatronics, Universidad Internacional del Ecuador, Av. Simon Bolivar, 170411, Quito, Ecuador

    Lorena Guachi-Guachi

Authors
  1. MacArthur Ortega-Bustamante
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Waldo Hasperué
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diego H. Peluffo-Ordóñez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daisy Imbaquingo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hind Raki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yahya Aalaila
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mouad Elhamdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lorena Guachi-Guachi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hind Raki .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Monash University, Clayton, VIC, Australia

    David Taniar

  4. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  5. University of Minho, Braga, Portugal

    Ana Cristina Braga

  6. University of Cagliari, Cagliari, Italy

    Chiara Garau

  7. National Technical University of Athens, Athens, Greece

    Anastasia Stratigea

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ortega-Bustamante, M. et al. (2023). Interactive Information Visualization Models: A Systematic Literature Review. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-36805-9_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36804-2

  • Online ISBN: 978-3-031-36805-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation