skip to main content
10.1145/3583961.3583969acmotherconferencesArticle/Chapter ViewAbstractPublication PagesihmConference Proceedingsconference-collections
research-article

Catalogue Visu: a Tool for Fast Visualization Prototyping

Published:29 May 2023Publication History

ABSTRACT

Visualization is an essential tool for exploring, understanding and presenting data. However, its use is limited by knowledge and tools. Novice users in visualization do not always know which visualization to choose for their data. The realization of a visualization with specific tools is often long and complex. In this article we present Catalogue Visu, an application allowing the free and fast realization of visualizations. Our main contribution is the design of the choice of the visualization type, which allows any user to choose an appropriate visualization for his needs. This method, the heart of Catalogue Visu, is iteratively validated by three user studies. Catalog Visu is accessible online and in permanent evolution, and is already used internally as a prototyping tool.

References

  1. Hanene Azzag, Fabien Picarougne, Christiane Guinot, and Gilles Venturini. 2006. Vrminer: A tool for multimedia database mining with virtual reality. In Processing and Managing Complex Data for Decision Support. IGI Global, 318–339.Google ScholarGoogle ScholarCross RefCross Ref
  2. Jacques Bertin and M Barbut. 1967. Sémiologie Graphique. Les diagrammes, les réseaux, les cartes.Google ScholarGoogle Scholar
  3. Fatma Bouali, Abdelheq Et-tahir Guettala, and Gilles Venturini. 2015. VizAssist: an interactive user assistant for visual data mining. The Visual Computer 32 (05 2015). https://doi.org/10.1007/s00371-015-1132-9Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Sylvain Bouveret, Renaud Blanch, Antoinette Baujard, François Durand, Herrade Igersheim, Jérôme Lang, Annick Laruelle, Jean-François Laslier, Isabelle Lebon, and Vincent Merlin. 2019. Voter Autrement 2017 - In Situ Experiment. (2019), 22 pages. https://doi.org/10.5281/zenodo.3548573Google ScholarGoogle ScholarCross RefCross Ref
  5. Stuart Card. 2002. Information Visualization. L. Erlbaum Associates Inc., USA, 544–582.Google ScholarGoogle Scholar
  6. Stuart Card, Jock Mackinlay, and Ben Shneiderman. 1999. Readings in Information Visualization: Using Vision To Think. Journal Abbreviation: Information Visualization - IVS Publication Title: Information Visualization - IVS.Google ScholarGoogle Scholar
  7. Remco Chang, Tera Marie Green, William Ribarsky, and et al. 2009. Defining Insight for Visual Analytics.Google ScholarGoogle Scholar
  8. Yang Chen, Jing Yang, and William Ribarsky. 2009. Toward effective insight management in visual analytics systems. In 2009 IEEE Pacific Visualization Symposium. IEEE, 49–56.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Eun Kyoung Choe, Bongshin Lee, 2015. Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE computer graphics and applications 35, 4 (2015), 28–37.Google ScholarGoogle Scholar
  10. Sarah Goodwin, Jason Dykes, Sara Jones, Iain Dillingham, Graham Dove, Alison Duffy, Alexander Kachkaev, Aidan Slingsby, and Jo Wood. 2013. Creative User-Centered Visualization Design for Energy Analysts and Modelers. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2516–2525. https://doi.org/10.1109/TVCG.2013.145Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Kyle Wm. Hall, Anthony Kouroupis, Anastasia Bezerianos, Danielle Albers Szafir, and Christopher Collins. 2022. Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines. IEEE Transactions on Visualization and Computer Graphics 28, 1 (Jan. 2022), 654–664. https://doi.org/10.1109/TVCG.2021.3114805Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Alaul Islam, Anastasia Bezerianos, Bongshin Lee, Tanja Blascheck, and Petra Isenberg. 2020. Visualizing Information on Watch Faces: A Survey with Smartwatch Users. In 2020 IEEE Visualization Conference (VIS). 156–160. https://doi.org/10.1109/VIS47514.2020.00038Google ScholarGoogle ScholarCross RefCross Ref
  13. Pawandeep Kaur and Michael Owonibi. 2017. A review on visualization recommendation strategies. In International Conference on Information Visualization Theory and Applications, Vol. 4. SCITEPRESS, 266–273.Google ScholarGoogle ScholarCross RefCross Ref
  14. Alicia Key, Bill Howe, Daniel Perry, and Cecilia Aragon. 2012. Vizdeck: self-organizing dashboards for visual analytics. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. 681–684.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ian Li, Anind K. Dey, and Jodi Forlizzi. 2012. Using Context to Reveal Factors That Affect Physical Activity. ACM Trans. Comput.-Hum. Interact. 19, 1, Article 7 (may 2012), 21 pages. https://doi.org/10.1145/2147783.2147790Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Zhengliang Liu, R. Crouser, and Alvitta Ottley. 2020. Survey on Individual Differences in Visualization. Computer Graphics Forum 39 (06 2020), 693–712. https://doi.org/10.1111/cgf.14033Google ScholarGoogle ScholarCross RefCross Ref
  17. Ivan Logre and Anne-Marie Déry-Pinna. 2018. MDE in Support of Visualization Systems Design: a Multi-Staged Approach Tailored for Multiple Roles. Proceedings of the ACM on Human-Computer Interaction 2, EICS (2018), 1–17.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jock Mackinlay. 1986. Automating the design of graphical presentations of relational information. Acm Transactions On Graphics (Tog) 5, 2 (1986), 110–141.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Eamonn James Maguire. 2014. Systematising glyph design for visualization. Ph.D. Dissertation. University of Oxford.Google ScholarGoogle Scholar
  20. Belgin Mutlu, Eduardo Veas, and Christoph Trattner. 2016. Vizrec: Recommending personalized visualizations. ACM Transactions on Interactive Intelligent Systems (TiiS) 6, 4 (2016), 1–39.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. C. North. 2006. Toward measuring visualization insight. IEEE Computer Graphics and Applications 26, 3 (May 2006), 6–9. https://doi.org/10.1109/MCG.2006.70Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Catherine Plaisant, Jean-Daniel Fekete, and Georges Grinstein. 2008. Promoting Insight-Based Evaluation of Visualizations: From Contest to Benchmark Repository. IEEE Transactions on Visualization and Computer Graphics 14, 1 (Jan. 2008), 120–134. https://doi.org/10.1109/TVCG.2007.70412 Conference Name: IEEE Transactions on Visualization and Computer Graphics.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Severino Ribecca. 2013. DataVizCatalog visual function taxonomy. https://datavizcatalogue.com/search.html. Accessed: 2020-09-29.Google ScholarGoogle Scholar
  24. P. Saraiya, C. North, and K. Duca. 2005. An insight-based methodology for evaluating bioinformatics visualizations. IEEE Transactions on Visualization and Computer Graphics 11, 4 (July 2005), 443–456. https://doi.org/10.1109/TVCG.2005.53 Conference Name: IEEE Transactions on Visualization and Computer Graphics.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Hans-Jörg Schulz, Thomas Nocke, Magnus Heitzler, and Heidrun Schumann. 2013. A design space of visualization tasks. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2366–2375.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Ben Shneiderman. 1996. The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings 1996 IEEE symposium on visual languages. IEEE, 336–343.Google ScholarGoogle ScholarCross RefCross Ref
  27. Melanie Tory and Torsten Moller. 2004. Rethinking visualization: A high-level taxonomy. In IEEE Symposium on information visualization. IEEE, 151–158.Google ScholarGoogle ScholarCross RefCross Ref
  28. Andrea Vázquez-Ingelmo, Francisco J García-Peñalvo, and Roberto Therón. 2019. Capturing high-level requirements of information dashboards’ components through meta-modeling. In Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality. 815–821.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Fernanda B Viegas, Martin Wattenberg, Frank Van Ham, Jesse Kriss, and Matt McKeon. 2007. Manyeyes: a site for visualization at internet scale. IEEE transactions on visualization and computer graphics 13, 6 (2007), 1121–1128.Google ScholarGoogle Scholar
  30. Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2015. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE transactions on visualization and computer graphics 22, 1 (2015), 649–658.Google ScholarGoogle Scholar
  31. Angela Zoss. 2017. Data visualization: visualization types. Duke Uni (2017). https://guides.library.duke.edu/datavis/vis_typesGoogle ScholarGoogle Scholar

Index Terms

  1. Catalogue Visu: a Tool for Fast Visualization Prototyping

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        IHM '23: Proceedings of the 34th Conference on l'Interaction Humain-Machine
        April 2023
        288 pages
        ISBN:9781450398244
        DOI:10.1145/3583961

        Copyright © 2023 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 May 2023

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate103of199submissions,52%
      • Article Metrics

        • Downloads (Last 12 months)22
        • Downloads (Last 6 weeks)2

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format