Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

What User Behaviors Make the Differences During the Process of Visual Analytics? (2311.00690v3)

Published 1 Nov 2023 in cs.HC, cs.CV, and cs.LG

Abstract: The understanding of visual analytics process can benefit visualization researchers from multiple aspects, including improving visual designs and developing advanced interaction functions. However, the log files of user behaviors are still hard to analyze due to the complexity of sensemaking and our lack of knowledge on the related user behaviors. This work presents a study on a comprehensive data collection of user behaviors, and our analysis approach with time-series classification methods. We have chosen a classical visualization application, Covid-19 data analysis, with common analysis tasks covering geo-spatial, time-series and multi-attributes. Our user study collects user behaviors on a diverse set of visualization tasks with two comparable systems, desktop and immersive visualizations. We summarize the classification results with three time-series machine learning algorithms at two scales, and explore the influences of behavior features. Our results reveal that user behaviors can be distinguished during the process of visual analytics and there is a potentially strong association between the physical behaviors of users and the visualization tasks they perform. We also demonstrate the usage of our models by interpreting open sessions of visual analytics, which provides an automatic way to study sensemaking without tedious manual annotations.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (61)
  1. Covid-19 data repository. https://github.com/CSSEGISandData/COVID-19.
  2. Low-level components of analytic activity in information visualization. In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., pp. 111–117, 2005. doi: 10 . 1109/INFVIS . 2005 . 1532136
  3. Space to think: Large high-resolution displays for sensemaking. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, pp. 55–64, 2010. doi: 10 . 1145/1753326 . 1753336
  4. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery, 31(3):606–660, 2017.
  5. There is no spoon: Evaluating performance, space use, and presence with expert domain users in immersive analytics. IEEE Transactions on Visualization and Computer Graphics, 26(1):536–546, 2020. doi: 10 . 1109/TVCG . 2019 . 2934803
  6. L. Battle and J. Heer. Characterizing exploratory visual analysis: A literature review and evaluation of analytic provenance in tableau. Computer Graphics Forum, 38(3):145–159. doi: 10 . 1111/cgf . 13678
  7. Vistrails: enabling interactive multiple-view visualizations. In VIS 05. IEEE Visualization, 2005., pp. 135–142, Oct 2005. doi: 10 . 1109/VISUAL . 2005 . 1532788
  8. Exploration strategies for discovery of interactivity in visualizations. IEEE Transactions on Visualization and Computer Graphics, 25(2):1407–1420, 2019. doi: 10 . 1109/TVCG . 2018 . 2802520
  9. M. Brehmer and T. Munzner. A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12):2376–2385, 2013. doi: 10 . 1109/TVCG . 2013 . 124
  10. A review of guidance approaches in visual data analysis: A multifocal perspective. Computer Graphics Forum, 38:861, 06 2019. doi: 10 . 1111/cgf . 13730
  11. Click2annotate: Automated insight externalization with rich semantics. In 2010 IEEE Symposium on Visual Analytics Science and Technology, pp. 155–162, 2010. doi: 10 . 1109/VAST . 2010 . 5652885
  12. Time series feature extraction on basis of scalable hypothesis tests (tsfresh–a python package). Neurocomputing, 307:72–77, 2018.
  13. Comparing and combining interaction data and eye-tracking data for the real-time prediction of user cognitive abilities in visualization tasks. ACM Trans. Interact. Intell. Syst., 10(2), may 2020. doi: 10 . 1145/3301400
  14. Imaxes: Immersive axes as embodied affordances for interactive multivariate data visualisation. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology, UIST ’17, pp. 71–83. ACM, New York, NY, USA, 2017. doi: 10 . 1145/3126594 . 3126613
  15. T. J. Crooks. Assessing student performance. Kensington, N.S.W., 1988.
  16. F. Dabek and J. J. Caban. A grammar-based approach for modeling user interactions and generating suggestions during the data exploration process. IEEE Transactions on Visualization and Computer Graphics, 23(1):41–50, 2017. doi: 10 . 1109/TVCG . 2016 . 2598471
  17. The ucr time series archive. IEEE/CAA Journal of Automatica Sinica, 6(6):1293–1305, 2019.
  18. Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery, 34(5):1454–1495, 2020.
  19. Beyond tasks: An activity typology for visual analytics. IEEE Transactions on Visualization and Computer Graphics, 24(1):267–277, 2018. doi: 10 . 1109/TVCG . 2017 . 2745180
  20. Vista: Integrating machine intelligence with visualization to support the investigation of think-aloud sessions. IEEE Transactions on Visualization and Computer Graphics, 26(1):343–352, 2020. doi: 10 . 1109/TVCG . 2019 . 2934797
  21. Hindsight: Encouraging exploration through direct encoding of personal interaction history. IEEE Transactions on Visualization and Computer Graphics, 23(1):351–360, 2017. doi: 10 . 1109/TVCG . 2016 . 2599058
  22. Exploring the sensemaking process through interactions and fnirs in immersive visualization. IEEE Transactions on Visualization and Computer Graphics, 27(5):2714–2724, 2021. doi: 10 . 1109/TVCG . 2021 . 3067693
  23. P. Good. Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer Science & Business Media, 2013.
  24. D. Gotz and M. X. Zhou. Characterizing users’ visual analytic activity for insight provenance. In 2008 IEEE Symposium on Visual Analytics Science and Technology, pp. 123–130, Oct 2008. doi: 10 . 1109/VAST . 2008 . 4677365
  25. Building and applying a human cognition model for visual analytics. Information Visualization, 8(1):1–13, 2009. doi: 10 . 1057/ivs . 2008 . 28
  26. D. P. Groth and K. Streefkerk. Provenance and annotation for visual exploration systems. IEEE Transactions on Visualization and Computer Graphics, 12(6):1500–1510, Nov. 2006. doi: 10 . 1109/TVCG . 2006 . 101
  27. A case study using visualization interaction logs and insight metrics to understand how analysts arrive at insights. IEEE Transactions on Visualization and Computer Graphics, 22(1):51–60, 2016. doi: 10 . 1109/TVCG . 2015 . 2467613
  28. Interest driven navigation in visualization. IEEE Transactions on Visualization and Computer Graphics, 18(10):1744–1756, 2012. doi: 10 . 1109/TVCG . 2012 . 23
  29. A model for the visualization exploration process. In IEEE Visualization, 2002. VIS 2002., pp. 323–330, 2002. doi: 10 . 1109/VISUAL . 2002 . 1183791
  30. A model and framework for visualization exploration. IEEE Transactions on Visualization and Computer Graphics, 13(2):357–369, 2007. doi: 10 . 1109/TVCG . 2007 . 28
  31. 1d convolutional neural networks and applications: A survey. Mechanical systems and signal processing, 151:107398, 2021.
  32. Using machine learning to infer reasoning provenance from user interaction log data: Based on the data/frame theory of sensemaking. Journal of Cognitive Engineering and Decision Making, 11(1):23–41, 2017. doi: 10 . 1177/1555343416672782
  33. A study of layout, rendering, and interaction methods for immersive graph visualization. IEEE Transactions on Visualization and Computer Graphics, 22(7):1802–1815, 2016.
  34. Beyond mouse and keyboard: Expanding design considerations for information visualization interactions. IEEE Transactions on Visualization and Computer Graphics, 18(12):2689–2698, 2012. doi: 10 . 1109/TVCG . 2012 . 204
  35. A survey on long short-term memory networks for time series prediction, 05 2020. doi: 10 . 13140/RG . 2 . 2 . 36761 . 65129/1
  36. Z. Liu and J. Stasko. Mental models, visual reasoning and interaction in information visualization: A top-down perspective. IEEE Transactions on Visualization and Computer Graphics, 16(6):999–1008, 2010. doi: 10 . 1109/TVCG . 2010 . 177
  37. Patterns and sequences: Interactive exploration of clickstreams to understand common visitor paths. IEEE Transactions on Visualization and Computer Graphics, 23(1):321–330, 2017. doi: 10 . 1109/TVCG . 2016 . 2598797
  38. Building multiple coordinated spaces for effective immersive analytics through distributed cognition. In 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA), pp. 1–11, 2018. doi: 10 . 1109/BDVA . 2018 . 8533893
  39. Permutation-based identification of important biomarkers for complex diseases via machine learning models. Nature communications, 12(1):1–12, 2021.
  40. P. Pirolli and S. Card. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. 01 2005.
  41. Characterizing provenance in visualization and data analysis: An organizational framework of provenance types and purposes. IEEE Transactions on Visualization and Computer Graphics, 22(1):31–40, Jan 2016. doi: 10 . 1109/TVCG . 2015 . 2467551
  42. Dataless sharing of interactive visualization. IEEE Transactions on Visualization and Computer Graphics, 2020.
  43. Effects of display size and resolution on user behavior and insight acquisition in visual exploration. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI ’15, p. 2759–2768. Association for Computing Machinery, New York, NY, USA, 2015. doi: 10 . 1145/2702123 . 2702406
  44. Modeling and evaluating user behavior in exploratory visual analysis. Information Visualization, 15(4):325–339, 2016. doi: 10 . 1177/1473871616638546
  45. Time series analysis and its applications, vol. 3. Springer, 2000.
  46. K. Smagulova and A. James. A survey on lstm memristive neural network architectures and applications. The European Physical Journal Special Topics, 228, 05 2019. doi: 10 . 1140/epjst/e2019-900046-x
  47. Predicting visual search task success from eye gaze data as a basis for user-adaptive information visualization systems. ACM Trans. Interact. Intell. Syst., 11(2), may 2021. doi: 10 . 1145/3446638
  48. Predicting visual search task success from eye gaze data as a basis for user-adaptive information visualization systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(2):1–25, 2021.
  49. User-adaptive information visualization: Using eye gaze data to infer visualization tasks and user cognitive abilities. In Proceedings of the 2013 International Conference on Intelligent User Interfaces, IUI ’13, p. 317–328. Association for Computing Machinery, New York, NY, USA, 2013. doi: 10 . 1145/2449396 . 2449439
  50. Inferring visualization task properties, user performance, and user cognitive abilities from eye gaze data. ACM Trans. Interact. Intell. Syst., 4(2), jul 2014. doi: 10 . 1145/2633043
  51. Pupillometry and head distance to the screen to predict skill acquisition during information visualization tasks. In Proceedings of the 22nd International Conference on Intelligent User Interfaces, IUI ’17, p. 221–231. Association for Computing Machinery, New York, NY, USA, 2017. doi: 10 . 1145/3025171 . 3025187
  52. Towards facilitating user skill acquisition: Identifying untrained visualization users through eye tracking. In Proceedings of the 19th International Conference on Intelligent User Interfaces, IUI ’14, p. 105–114. Association for Computing Machinery, New York, NY, USA, 2014. doi: 10 . 1145/2557500 . 2557524
  53. A virtual reality visualization tool for neuron tracing. IEEE Transactions on Visualization and Computer Graphics, 24(1):994–1003, Jan 2018. doi: 10 . 1109/TVCG . 2017 . 2744079
  54. LIVVIL: Logging Interactive Visualizations and Visualizing Interaction Logs. 10 2016.
  55. Identifying redundancy and exposing provenance in crowdsourced data analysis. IEEE Transactions on Visualization and Computer Graphics, 19(12):2198–2206, 2013. doi: 10 . 1109/TVCG . 2013 . 164
  56. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE Transactions on Visualization and Computer Graphics, 22(1):649–658, 2016. doi: 10 . 1109/TVCG . 2015 . 2467191
  57. Survey on the analysis of user interactions and visualization provenance. Computer Graphics Forum, 39:757–783, 06 2020. doi: 10 . 1111/cgf . 14035
  58. Chart constellations: Effective chart summarization for collaborative and multi‐user analyses. Computer Graphics Forum, 37, 2018.
  59. Origin-destination flow maps in immersive environments. IEEE Transactions on Visualization and Computer Graphics, 2018. doi: 10 . 1109/TVCG . 2018 . 2865192
  60. Maps and Globes in Virtual Reality. Computer Graphics Forum, 2018. doi: 10 . 1111/cgf . 13431
  61. Chartnavigator: An interactive pattern identification and annotation framework for charts. IEEE Transactions on Knowledge and Data Engineering, pp. 1–1, 2021. doi: 10 . 1109/TKDE . 2021 . 3094236
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Zekun Wu (20 papers)
  2. Shahin Doroudian (2 papers)
  3. Aidong Lu (12 papers)