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Usability Heuristic Evaluation of Scientific Data Analysis and Visualization Tools

  • Samar SwaidEmail author
  • Mnsa Maat
  • Hari Krishnan
  • Devarshi Ghoshal
  • Lavanya Ramakrishnan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 607)

Abstract

Scientific data analysis and visualization (DAV) tools are a critical component of the software ecosystem. The growth in data volumes in various scientific domains is resulting in technical innovations in DAV tools. Usability of these tools is becoming extremely critical to facilitate next-generation scientific discoveries. Usability heuristics is a widely applied approach to inspect software usability. We expand Nielsen’s heuristics to include two additional criteria: analytical-reasoning and customized-experience to capture the usability dimensions of DAV tools. We evaluated three tools to test the proposed heuristics and demonstrate its fit to DAV tools of varying features. The contribution of our work is threefold: (i) identify DAV usability heuristics, (ii) develop a framework to inspect DAV tools, (iii) conduct a comprehensive evaluation of the usability of selected DAV tools. We also provide a discussion on the heuristics framework for DAV tools and future work.

Keywords

Usability Heuristics evaluation Data analysis Visualization User-centered design Scientific software Walkthrough 

Notes

Acknowledgements

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number(s) DE-AC02-05CH11231, program manager Lucy Nowell.

References

  1. 1.
    Baven, N., MacLeod, M.: Usability measurement in context. Behav. Inf. Technol. 13, 132–145 (1994)CrossRefGoogle Scholar
  2. 2.
    Bogers, J., Morales, I., Rodriguez, N.: Guidelines for designing usable world wide web pages. In: CHI 1998 Conference, pp. 277–278 (1990)Google Scholar
  3. 3.
    Bos, R., Gurp, J., Verpoorten, J., Brinkkemper, S.: Heurestics evaluations of content management systems: CMS specific heuristics. In: IADIS International Conference, pp. 247–254 (2005)Google Scholar
  4. 4.
    Chen, S., Macredie, R.: The assessment of usability of electronic shopping: a heuristics evaluation. Int. J. Inf. Manage. 25, 516–532 (2005)CrossRefGoogle Scholar
  5. 5.
    Dumas, J., Redish, J.: Practical Guide to Usability Testing. Intellect Books, Exeter (1999)Google Scholar
  6. 6.
    Ferre, X., Juristo, N., Windl, H., Constantine, L.: Usability basics for software developers. IEEE Softw. 8(1), 22–29 (2001)CrossRefGoogle Scholar
  7. 7.
    Gumussoy, C.: Usability guidelines for banking software design. Comput. Hum. Behav. 62, 277–285 (2016)CrossRefGoogle Scholar
  8. 8.
    Hilbert, D., Redmiles, D.: Extracting usability information from user interface events. ACM Comput. Surv. 32(4), 384–421 (2000)CrossRefGoogle Scholar
  9. 9.
    Hoashi, K., Hamawaki, S., Ishizaki, H., Takishima, Y., Katto, J.: Usability evaluation of visualization interfaces for content-based music retrieval systems. In: The 10th International Society for Music Information Retrieval Conference (2009)Google Scholar
  10. 10.
    Howells, L.: A Guide to heuristics Website Reviews (2011). https://www.smashingmagazine.com
  11. 11.
    ISO 9241-11: Guidelines for Specifying and Measuring usability (1998)Google Scholar
  12. 12.
    ISO/IEC: ISO9126. Information Technology–Software Quality Characteristics and Metrics (1991)Google Scholar
  13. 13.
    Jacobsen, N., Hertzum, M., John, B.: The Evaluator effect in usability tests. In: CHI 1998 Summary: Human Factors in Computing Systems, pp. 255–256 (1998)Google Scholar
  14. 14.
    Jaferian, P., Hawkey, K., Sotirakopoulos, A., Velez-Rojas, M., Beznosov, K.: Heuristics for evaluating IT security management tools. Hum.-Comput. Interact. 29, 311–350 (2014)CrossRefGoogle Scholar
  15. 15.
    Johnson, C.: Top scientific visualization research problems. IEEE Comput. Graph. Appl. 24(4), 13–17 (2004)CrossRefGoogle Scholar
  16. 16.
    Jupyter (2016). http://jupyter.org
  17. 17.
    Keim, D., Mansmann, F., Stoffel, A., Ziegler, H.: Visual Analytics (2008). https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/KeMaSt08.pdf
  18. 18.
    List, M., Ebert, P., Albrecht, F.: Ten simple rules for developing usable software in computational biology. J. Comput. Biol. 13(1) (2017)Google Scholar
  19. 19.
    LLNL. Visit User manual (2005). https://wci.llnl.gov/content/assets/docs
  20. 20.
    Macaulay, C., Sloan, D., Jiang, X., Forbes, P., Loynton, S., Swedlow, J., Gregor, P.: Usability and user-centered design in scientific software development. IEEE Comput. Soc. 96–101 (2009)Google Scholar
  21. 21.
    Mankoff, J., Dey, A., Hsieh, G., Kientz, J., Lederer, S., Ames, M.: Heuristics evaluation of ambient displays. In: Proceedings of the CHI 2003 Conference in Human Factors in Computer Systems (2003)Google Scholar
  22. 22.
    Marcus, A., Comorski, D., Sergeyev, A.: Supporting the evolution of a software visulization tool through usability studies. In: IEEE Computer Society: The 13th International Workshop on Program Comprehension (2005)Google Scholar
  23. 23.
    Muller, M., Matheson, L., Page, C., Gallup, R.: Participatory heuristics evaluation. Interaction 5, 13–18 (1998)CrossRefGoogle Scholar
  24. 24.
    Nardi, B.: Activity Theory in Human-Computer Interaction. Morgan Claypool, San Rafael (1995)Google Scholar
  25. 25.
    Nielsen, J.: Enhancing the explanatory power of usability heuristics. In: CAN CHI 1994, pp. 152–158. ACM Press, New York (1994)Google Scholar
  26. 26.
    Nielsen, J.: Finding usability problems through heuristics evaluation. In: CHI 1992, pp. 373–380. ACM Press, New York (1992)Google Scholar
  27. 27.
    Nielsen, J.: Severity Rankings for Usability Problems (1995). http://www.useit.com/papers/heuristic/severityrating.html
  28. 28.
    Nielsen, J.: Enhancing the explanatory power of usability heuristics. In: Proceedings of the ACM CHI 1994 Conference, Boston, MA, pp. 152–158, 24–28 April 1994Google Scholar
  29. 29.
    Nielsen, J.: Usability heuristics. In: Usability Engineering. Academic Press, San Diego (1993)Google Scholar
  30. 30.
    Nielsen, J., Molich, R.: Heuristics evaluation for user interface. In: Proceedings of the CHI 1990 Conference on Human Factors in Computer Systems (1990)Google Scholar
  31. 31.
    Norma, D., Draper, S. (eds.). User Centered System Design: New Perspectives on Human-Computer Interaction, pp. 31–61. Erlbaum, Hillside (1986)Google Scholar
  32. 32.
    Plaisant, C.: The challenge of information visualization evaluation. In: Advanced Visual Interfaces, pp. 109–116, May 2004Google Scholar
  33. 33.
    Rampersad, L.: Usability in Scientific and Astronomy Software (2016). Retrieved from: http://calvinbrizzi.com/visastro/docs/rmplau001_review.pdf
  34. 34.
    Rubin, J., Chisnell, D.: Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests, 2nd edn. Wiley Publishing Inc., Indianapolis (2008)Google Scholar
  35. 35.
    Rusu, C., Roncagliolo, S., Tapia, G., Hayvar, D., Rusu, V., Gorgan, D.: Usability heuristics for grid computing applications. In: Proceedings ACHI, pp. 53–58 (2011)Google Scholar
  36. 36.
    Shneiderman, B.: Dynamic queries for visual information seeking. Softw. IEEE 11, 70–77 (1994)CrossRefGoogle Scholar
  37. 37.
    Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: IEEE Symposium on Visual Languages, pp. 336–343 (1996)Google Scholar
  38. 38.
    Shneiderman, B.: Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley Publishing Co., Reading (1987)Google Scholar
  39. 39.
    Shrinivasan, Y., Wijk, J.: Supporting the Analytical Reasoning Process in Information Visualization (2008)Google Scholar
  40. 40.
    Scotch, M., Parmanto, B., Monaco, V.: Usability evaluation of the Spatial OLAP Visualization and Analysis Tool (SOVAT). J. Usability Stud. 7(2), 76–95 (2007)Google Scholar
  41. 41.
    Sutcliff, A., Gault, B.: Heuristics evaluation of virtual reality applications. Interact. Comput. 16, 381–849 (2004)Google Scholar
  42. 42.
    Swaid, S.: A novel strategy to improve STEM education: The E-science approach. In: Wang, V. (ed.) Encyclopedia of Education and Technology in 2014. The Handbook of Research on Education and Technology in a Changing Society. IGI Global. Hershey, Pennsylvania (2014)Google Scholar
  43. 43.
    Swaid, S., Wigand, R.: Measuring the quality of E-service: scale development and initial validation. J. Electron. Commer. Res. 10(1), 13–28 (2009)Google Scholar
  44. 44.
    Tory, M., Moller, T.: Evaluation visualizations: do expert reviews work. IEEE Comput. Graph. Appl. 25(5), 8–11 (2005)CrossRefGoogle Scholar
  45. 45.
    UVCDAT: Gallery (2016). http://uvcdat.llnl.gov/gallery.html
  46. 46.
    VisItUser.org. Key-framing Animation (2016). http://visitusers.org
  47. 47.
    Wharton, C., Rieman, J., Lewis, C., Polson, P.: The cognitive walkthrough method: A practitioner’s guide. In: Nielsen, J., Mack, R. (eds.) Usability Inspection Methods. Wiley, New York (1994)Google Scholar
  48. 48.
    Zhang, J., Johnson, T., Patel, V., Paige, D., Kubose, T.: Using usability heuristics to evaluate patient safety of medical devices. J. Biomed. Inform. 36, 23–30 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Samar Swaid
    • 1
    Email author
  • Mnsa Maat
    • 1
  • Hari Krishnan
    • 2
  • Devarshi Ghoshal
    • 2
  • Lavanya Ramakrishnan
    • 2
  1. 1.Department of Applied Math and Computer SciencePhilander Smith CollegeLittle RockUSA
  2. 2.Data Science and Technology DepartmentLawrence Berkeley National LaboratoryBerkeleyUSA

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