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Animated narrative visualization for video clickstream data

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Published:28 November 2016Publication History

ABSTRACT

Video clickstream data are important for understanding user behaviors and improving online video services. Various visual analytics techniques have been proposed to explore patterns in these data. However, those techniques are mainly developed for analysis and do not sufficiently support presentations. It is still difficult for data analysts to convey their findings to an audience without prior knowledge. In this paper, we propose to use animated narrative visualization to present video clickstream data. Compared with traditional methods which directly turn click events into animations, our animated narrative visualization focuses on conveying the patterns in the data to a general audience and adopts two novel designs, non-linear time mapping and foreshadowing, to make the presentation more engaging and interesting. Our non-linear time mapping method keeps the interesting parts as the focus of the animation while compressing the uninteresting parts as the context. The foreshadowing techniques can engage the audience and alert them to the events in the animation. Our user study indicates the effectiveness of our system and provides guidelines for the design of similar systems.

References

  1. Aguiar, E., Nagrecha, S., and Chawla, N. V. 2015. Predicting online video engagement using clickstreams. In Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on, IEEE, 1--10.Google ScholarGoogle Scholar
  2. Aigner, W., Miksch, S., Müller, W., Schumann, H., and Tominski, C. 2007. Visualizing time-oriented dataa systematic view. Computers & Graphics 31, 3, 401--409. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Amini, F., Henry Riche, N., Lee, B., Hurter, C., and Irani, P. 2015. Understanding data videos: Looking at narrative visualization through the cinematography lens. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, ACM, 1459--1468. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bach, B., Pietriga, E., and Fekete, J.-D. 2014. Graphdiaries: animated transitions andtemporal navigation for dynamic networks. Visualization and Computer Graphics, IEEE Transactions on 20, 5, 740--754. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Beal, C. R., and Cohen, P. R. 2008. Temporal data mining for educational applications. In PRICAI 2008: Trends in Artificial Intelligence. Springer, 66--77. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bordwell, D., Thompson, K., and Ashton, J. 1997. Film art: An introduction, vol. 7. McGraw-Hill New York.Google ScholarGoogle Scholar
  7. Chorianopoulos, K. 2013. Collective intelligence within web video. Human-centric Computing and Information Sciences 3, 1, 1--16.Google ScholarGoogle ScholarCross RefCross Ref
  8. Dachselt, R., and Weiland, M. 2006. Timezoom: a flexible detail and context timeline. In CHI'06 Extended Abstracts on Human Factors in Computing Systems, ACM, 682--687. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Dragicevic, P., Bezerianos, A., Javed, W., Elmqvist, N., and Fekete, J.-D. 2011. Temporal distortion for animated transitions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 2009--2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Foreshadowing.org, 2016. Types of foreshadowing. http://foreshadowing.org/types-of-foreshadowing.html. Retrived on April 27th, 2016.Google ScholarGoogle Scholar
  11. Fulda, J., Brehmer, M., and Munzner, T. 2016. Time-linecurator: Interactive authoring of visual timelines from unstructured text. Visualization and Computer Graphics, IEEE Transactions on 22, 1, 300--309.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Gratzl, S., Lex, A., Gehlenborg, N., Cosgrove, N., and Streit, M. 2016. From visual exploration to storytelling and back again. bioRxiv, 049585.Google ScholarGoogle Scholar
  13. Heer, J., and Robertson, G. G. 2007. Animated transitions in statistical data graphics. Visualization and Computer Graphics, IEEE Transactions on 13, 6, 1240--1247. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Hou, X., and Zhang, L. 2007. Saliency detection: A spectral residual approach. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, IEEE, 1--8.Google ScholarGoogle Scholar
  15. Huron, S., Vuillemot, R., and Fekete, J.-D. 2013. Visual sedimentation. Visualization and Computer Graphics, IEEE Transactions on 19, 12, 2446--2455. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Lee, J., Podlaseck, M., Schonberg, E., and Hoch, R. 2001. Visualization and analysis of clickstream data of online stores for understanding web merchandising. In Applications of Data Mining to Electronic Commerce. Springer, 59--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. McKee, R. 1997. Substance, Structure, Style, and the Principles of Screenwriting. New York: HarperCollins.Google ScholarGoogle Scholar
  18. Mediacollege, 2016. Manipulating time in video production. http://www.mediacollege.com/video/editing/. Retrieved on March 10th, 2016.Google ScholarGoogle Scholar
  19. Montgomery, A. L., Li, S., Srinivasan, K., and Liechty, J. C. 2004. Modeling online browsing and path analysis using clickstream data. Marketing Science 23, 4, 579--595.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Nguyen, P. H., Xu, K., Walker, R., and Wong, B. 2014. Schemaline: Timeline visualization for sensemaking. In Information Visualisation (IV), 2014 18th International Conference on, IEEE, 225--233.Google ScholarGoogle Scholar
  21. Riedl, M. O., and Young, R. M. 2010. Narrative planning: Balancing plot and character. Journal of Artificial Intelligence Research 39, 1, 217--268. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Robertson, G., Fernandez, R., Fisher, D., Lee, B., and Stasko, J. 2008. Effectiveness of animation in trend visualization. Visualization and Computer Graphics, IEEE Transactions on 14, 6, 1325--1332. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Rosling, H. 2009. Gapminder. GapMinder Foundation http://www.gapminder.org, 91.Google ScholarGoogle Scholar
  24. Segel, E., and Heer, J. 2010. Narrative visualization: Telling stories with data. Visualization and Computer Graphics, IEEE Transactions on 16, 6, 1139--1148. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Shi, C., Fu, S., Chen, Q., and Qu, H. 2015. Vismooc: Visualizing video clickstream data from massive open online courses. In Visualization Symposium (PacificVis), 2015 IEEE Pacific, IEEE, 159--166.Google ScholarGoogle Scholar
  26. Sigovan, C., Muelder, C. W., and Ma, K.-L. 2013. Visualizing large-scale parallel communication traces using a particle animation technique. In Computer Graphics Forum, vol. 32, Wiley Online Library, 141--150. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Thomas, F., Johnston, O., and Thomas, F. 1995. The illusion of life: Disney animation. Hyperion New York.Google ScholarGoogle Scholar
  28. Tversky, B., Morrison, J. B., and Betrancourt, M. 2002. Animation: can it facilitate? International journal of humancomputer studies 57, 4, 247--262. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Waldner, M., Le Muzic, M., Bernhard, M., Purgathofer, W., and Viola, I. 2014. Attractive flicker - guiding attention in dynamic narrative visualizations. Visualization and Computer Graphics, IEEE Transactions on 20, 12, 2456--2465.Google ScholarGoogle Scholar
  30. Wei, J., Shen, Z., Sundaresan, N., and Ma, K.-L. 2012. Visual cluster exploration of web clickstream data. In Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, IEEE, 3--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Zhao, J., Drucker, S. M., Fisher, D., and Brinkman, D. 2012. Timeslice: Interactive faceted browsing of timeline data. In Proceedings of the International Working Conference on Advanced Visual Interfaces, ACM, 433--436. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      SA '16: SIGGRAPH ASIA 2016 Symposium on Visualization
      November 2016
      129 pages
      ISBN:9781450345477
      DOI:10.1145/3002151

      Copyright © 2016 ACM

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      Publication History

      • Published: 28 November 2016

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