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StoryFlow - Visually Tracking Evolution of Stories

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Storyline visualizations, which are useful in many applications, aim to illustrate the dynamic relationships between entities in a story. However, the growing complexity and scalability of stories pose great challenges for existing approaches. In this paper, we propose an efficient optimization approach to generating an aesthetically appealing storyline visualization, which effectively handles the hierarchical relationships between entities over time. The approach formulates the storyline layout as a novel hybrid optimization approach that combines discrete and continuous optimization. The discrete method generates an initial layout through the ordering and alignment of entities, and the continuous method optimizes the initial layout to produce the optimal one. The efficient approach makes real-time interactions (e.g., bundling and straightening) possible, thus enabling users to better understand and track how the story evolves.

This work was presented in IEEE InfoVis 2013.

Project page:
http://www.ycwu.org/projects/infovis13.html

Published in: Technology, Education
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StoryFlow - Visually Tracking Evolution of Stories

  1. 1. StoryFlow: Tracking the Evolution of Stories Shixia Liu, Yingcai Wu, Enxun Wei, Mengchen Liu, Yang Liu Microsoft Research Asia 1
  2. 2. Outline  Introduction  Optimization Framework  StoryFlow Layout  Interactive Exploration  Experiments  Conclusion
  3. 3. Outline  Introduction  Optimization Framework  StoryFlow Layout  Interactive Exploration  Experiments  Conclusion
  4. 4. Storytelling
  5. 5. Who, When, and Where
  6. 6. Stories Are Complicated  The dynamic relationships of characters
  7. 7. Randall Munroe’s Storyline Visualization
  8. 8. Storyline Visualization time
  9. 9. Storyline Visualization One character T-Rex Dinosaurs Human time
  10. 10. Storyline Visualization Five characters in the same scene Dinosaurs Human time
  11. 11. Storyline Visualization Dinosaurs Human time
  12. 12. Storyline Visualization time
  13. 13. Storyline Visualization Applications Tracing genealogical data Tracking community evolution Kim et al. 2010 Reda et al. 2011
  14. 14. General Storyline Layout  Yuzuru Tanahashi and Prof. Kwan-Liu Ma’s work Dreams inside dreams
  15. 15. Hierarchical Locations
  16. 16. StoryFlow  Real-time interactions  Level-of-detail rendering First debate  Location hierarchy VP debate Second debate Third debate
  17. 17. Outline  Introduction  Optimization Framework  StoryFlow Layout  Interactive Exploration  Experiments  Conclusion
  18. 18. System
  19. 19. Input Data  Location hierarchy  Session list
  20. 20. Objectives Crossings Wiggles White Space
  21. 21. Optimization Strategy Importance decrease Crossings Discrete Number of wiggles Wiggle distance Wiggles Wiggle distance Continuous White space
  22. 22. Outline  Introduction  Optimization Framework  StoryFlow Layout  Interactive Exploration  Experiments  Conclusion
  23. 23. Discrete and Continuous optimization  Discrete optimization  Continuous optimization – Edge crossings – Wiggle distance – Number of wiggles – White space
  24. 24. Hierarchy Generation Session list Location tree Relationship trees
  25. 25. Ordering 1. Sorting location nodes using a greedy algorithm from bottom to top 2. Ordering sessions based on a DAG barycenter sweeping algorithm
  26. 26. Alignment  Longest common subsequence ABCDEFG BCDGK BCDG
  27. 27. Compaction  Quadratic programming ne nt 1 ne i 1 i 1 j 1 nt Minimize ( yi , j  yi , j 1 )2   yi2, j j Subject to yi1 , j  yi2 , j , if Si1 , j  Si2 , j ; Line order yi , j  yi , j 1 , if Si , j  Si , j 1 ; Line alignment yi , j  yi 1, j  din , if SID( Si , j )  SID( Si 1, j ); Line adjacency yi , j  yi 1, j  d out , if SID( Si , j )  SID( Si 1, j ). Line separate
  28. 28. Outline  Introduction  Optimization Framework  StoryFlow Layout  Interactive Exploration  Experiments  Conclusion
  29. 29. System
  30. 30. User Interactions
  31. 31. User Interactions
  32. 32. User Interactions
  33. 33. User Interactions
  34. 34. Outline  Introduction  Optimization Framework  StoryFlow Layout  Interactive Exploration  Experiments  Conclusion
  35. 35. Evaluation 1 Quantitative Analysis 2 Movie Examples 3 Case Study
  36. 36. Quantitative Analysis  Intel i7-2600 CPU (3.4GHz)  8GB memory Data Time(s) Crossings Wiggles #Entity #Frame Ours GA Ours GA Ours GA Star Wars 14 50 0.16 129.79 48 93 82 133 Inception 8 71 0.16 149.67 23 99 88 162 Matrix 14 42 0.16 172.47 14 43 54 94 MID 79 523 0.60 >10^5 1267 1871 831 874 GA refers to Tanahashi and Ma’s method based on Genetic Algorithm (GA)
  37. 37. Our method GA method Randall’s work Jurassic Park (a)
  38. 38. Inception Our method GA method
  39. 39. Our method King Lear GA method
  40. 40. The Lord of the Rings Trilogy
  41. 41. US 2012 Presidential Election – 2012 US presidential election Twitter Data • 89,174,308 tweets from May 01, 2012 to November 20, 2012 • 900 users: politicians (334), media (288), and grassroots (276 ) • Two-level location hierarchy – Five hot topics: Welfare, Defense, Economy, Election, and Horse race – 2,344 hot hashtags • Session List ID Hashtag Start End Members 0 Hashtag1 140 167 Opinion leader A, Opinion leader B 1 Hashtag2 145 180 Opinion leader C, Opinion leader D
  42. 42. Overall Patterns (1/2)  Five significant events on Election – First debate, VP debate, second debate, and third debate Grassroots Media Political Figures Defense Election First debate VP debate Second debate Third debate Voting Economy Welfare Horse Race Timeline
  43. 43. Overall Patterns (2/2)  Three user groups focused mainly on Election – Grassroots also focused on Economy and switched frequently – Political figures were more focused – Media occasionally switched Grassroots Media Political Figures Defense Election Economy Welfare Horse Race Timeline
  44. 44. Significant Transition  Transition from Election to Economy Grassroots Media Political Figures Defense Election First debate VP debate Second debate Third debate Voting Economy Welfare Horse Race Timeline Sensata tlot teaparty gop think Romney is tough on china? ask the workers of #sensata about that as they train their Chinese replacements
  45. 45. Significant Transition  Transition from Election to Economy Grassroots Media Political Figures Defense Election First debate VP debate Second debate Third debate Voting Economy Welfare Horse Race Timeline Issue-attention cycle sandy fema
  46. 46. Outline  Introduction  Optimization Framework  StoryFlow Layout  Interactive Exploration  Experiments  Conclusion
  47. 47. Conclusion  A Storyline visualization system – An efficient hybrid optimization approach – A hierarchy-aware storyline layout – A method for interactively and progressively rendering  Future improvements – Flashback narrative
  48. 48. Acknowledgements  Prof. Jonathan J.H. Zhu @ CityU, Hong Kong  Prof. Tai-Quan Peng @ NTU, Singapore  Prof. Kwan-Liu Ma and Yuzuru Tanahashi @ UC Davis
  49. 49. Thank you Email: yingcai.wu@microsoft.com

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