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Visualization for Event Sequences Exploration

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Data Visualization Summit
                                          San Francisco, CA
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Visualization for Event Sequences Exploration

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My talk at the Data Visualization Summit in San Francisco April 11, 2013
http://theinnovationenterprise.com/summits/data-visualization-sf

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Abstract
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Many aspects of our lives can be captured and described as series of events, or event sequences. These event sequences can be keys to understanding many things: medical services, logistics, sports, user behavior, etc. In this presentation, I will talk about techniques for visualizing event sequences, from simple to advance, and also show examples that demonstrate the power of visualizations in exploring and understanding event sequences.

My talk at the Data Visualization Summit in San Francisco April 11, 2013
http://theinnovationenterprise.com/summits/data-visualization-sf

----------------
Abstract
----------------
Many aspects of our lives can be captured and described as series of events, or event sequences. These event sequences can be keys to understanding many things: medical services, logistics, sports, user behavior, etc. In this presentation, I will talk about techniques for visualizing event sequences, from simple to advance, and also show examples that demonstrate the power of visualizations in exploring and understanding event sequences.

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Visualization for Event Sequences Exploration

  1. 1. Data Visualization Summit San Francisco, CA Apr 11, 2013 Visualizations for Event Sequences Exploration Krist Wongsuphasawat Data Visualization Scientist Twitter, Inc. @kristw
  2. 2. event% event% event% event% event% event% event% event% Life event% event% event% event% event% event% event% event%
  3. 3. Time Event type% ( 7:00 am, Wake up ) event% event% event% event% event% event% event% event% Life event% event% event% event% event% event% event% event%
  4. 4. event% event% event% event% event% event% event% event% Life event% event% event% event% event% event% event% event% “Event Sequence”
  5. 5. Daily Activity 7:30 a.m. 7:45 a.m. 8:30 a.m. Wake Up Exercise Go to work
  6. 6. Traffic Incidents 9:30 a.m. 9:55 a.m. 10:30 a.m. Notification Units arrived Road cleared
  7. 7. http://timeline.national911memorial.org/
  8. 8. Event Sequences Medical Transportation Sports Education Web logs Logistics and more…
  9. 9. Outline ? u ences e nt seq hat are ev them? W is ualize Ho w to v a b ig dat Ap ply to
  10. 10. Visualization Techniques
  11. 11. Event glyphs timeline sequence
  12. 12. simple event sequence timeline.js Horizontal axis = time Glyphs = events http://timeline.verite.co/
  13. 13. Event glyphs timeline sequence + Interval
  14. 14. interval •  Car crash (point) time 10 a.m. •  Meeting (interval) 10 – 11 a.m.
  15. 15. interval >> width traffic incident CATT Lab, University of Maryland -- http://teachamerica.com/VIZ11/VIZ1102Pack/index.htm
  16. 16. interval >> width chronoline.js http://stoicloofah.github.io/chronoline.js/
  17. 17. Event glyphs timeline sequence + Interval width + Event types
  18. 18. types time Nurses’ actions Doctors’ actions They all look similar.
  19. 19. types time Nurses’ actions Doctors’ actions Better?
  20. 20. The path of protest types >> color http://www.guardian.co.uk/world/interactive/2011/mar/22/middle-east-protest-interactive-timeline
  21. 21. types >> colors + shapes http://timeglider.com/widget/ timeglider.js
  22. 22. Event glyphs timeline sequence + Interval width + Event colors shapes types High + density
  23. 23. high density time Too many overlaps and occlusions
  24. 24. high density >> facet Google Chrome loading scripting rendering & painting Facet Google Chrome > Developer Tools > Timeline
  25. 25. high density >> facet Lifelines http://www.cs.umd.edu/lifelines
  26. 26. high density >> binning British History Timeline bin by year
  27. 27. high density >> aggregation CloudLines Raw event data Kernel Density Estimation + Importance Func. + Truncation Encode cloud size
  28. 28. high density >> aggregation CloudLines (2) Krstajic, M., Bertini, E., & Keim, D. A. (2011). CloudLines: Compact Display of Event Episodes in Multiple Time-Series. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2432.
  29. 29. linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
  30. 30. circular timeline 2008 2009 2010 2011 2012 linear Dec Jan Feb Nov Mar circular Oct Apr repeating patterns Sep May Aug Jun Jul
  31. 31. circular timeline (2) Traffic Incidents VanDaniker, M. (2010). Leverage of Spiral Graph for Transportation System Data Visualization. Transportation Research Record: Journal of the Transportation Research Board, 2165, 79–88.
  32. 32. stacked timeline 2008 2009 2010 2011 2012 linear 2008 2009 2008 2009 2011 2010 2012 2010 2011 2012
  33. 33. stacked timeline (2) Tweet Volume Rios, M., & Lin, J. (2012). Distilling Massive Amounts of Data into Simple Visualizations : Twitter Case Studies. Proceedings of the Workshop on Social Media Visualization (SocMedVis) at ICWSM 2012 (pp. 22–25).
  34. 34. linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
  35. 35. collection 1 2 n Event Event ... Event sequence sequence sequence
  36. 36. collection multiple timelines Event sequence #1 Event sequence #2 Event sequence #3 Event sequence #4
  37. 37. collection 1 2 n Event Event ... Event sequence sequence sequence Millions!
  38. 38. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions
  39. 39. Interaction #1 align
  40. 40. Interaction #1 align
  41. 41. Interaction #1 align
  42. 42. Interaction #2 rank
  43. 43. Interaction #2 rank Rank by number of events or any criteria
  44. 44. Interaction #3 filter
  45. 45. Interaction #3 filter Select only event sequences with events Set your own filters
  46. 46. Interaction #4 group
  47. 47. Interaction #4 group 1 2 3 Group by sequence length or any clustering algorithm / properties
  48. 48. Interaction #5 search •  Simple search ABC –  Sequence matching AABCDEFGH –  Subsequence matching AXAYBZCED •  Regular Expression A B* (C|D)
  49. 49. Interaction #5 search (2) •  Dynamic X 50% C 75% AB Y 50% D 25%
  50. 50. Interaction #5 search (2) •  Dynamic X 70% D 50% ABC Y 30% E 50% •  Similarity search Similar to ABCD ABCD ABD ACE …
  51. 51. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search filter group
  52. 52. aggregation by time temporal summary Day 1 Day 2 Day 3 Day 4 Day 5 bin & count
  53. 53. aggregation by time temporal summary Wang, T. D., Plaisant, C., Shneiderman, B., Spring, N., Roseman, D., Marchand, G., Mukherjee, V., et al. (2009). Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison. IEEE Transactions on Visualization and Computer Graphics, 15(6), 1049–1056.
  54. 54. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group
  55. 55. aggregation by sequence LifeFlow e.g. 1) What happened to the patients after they arrived? Arrival! ? ? 2) What happened to the patients before & after ICU? ICU! ? ? ? ?
  56. 56. aggregation by sequence LifeFlow overview / summary Millions of records!
  57. 57. Demo LifeFlow Wongsuphasawat, K., Guerra Gómez, J. A., Plaisant, C., Wang, T. D., Taieb-Maimon, M., & Shneiderman, B. (2011). LifeFlow: Visualizing an Overview of Event Sequences. Proceedings of CHI'2011 (pp. 1747–1756).
  58. 58. Demo LifeFlow Wongsuphasawat, K., Guerra Gómez, J. A., Plaisant, C., Wang, T. D., Taieb-Maimon, M., & Shneiderman, B. (2011). LifeFlow: Visualizing an Overview of Event Sequences. Proceedings of CHI'2011 (pp. 1747–1756).
  59. 59. Demo LifeFlow Wongsuphasawat, K., Guerra Gómez, J. A., Plaisant, C., Wang, T. D., Taieb-Maimon, M., & Shneiderman, B. (2011). LifeFlow: Visualizing an Overview of Event Sequences. Proceedings of CHI'2011 (pp. 1747–1756).
  60. 60. aggregation by sequence LifeFlow profile! home! start! home! photos! home! contact! home!
  61. 61. aggregation by sequence Google Analytics profile! start! home! photos! home! contact! http://www.google.com/analytics
  62. 62. aggregation by sequence Google Analytics profile! home! start! home! photos! videos! contact! http://www.google.com/analytics
  63. 63. aggregation by sequence Google Analytics top pages only height = number of visits http://www.google.com/analytics
  64. 64. Event + Outcome sequence
  65. 65. Time% Game #1 Win (1) 10th minute 25th minute 90th minute Goal Concede Goal or any sports
  66. 66. Time% Game #1 Win (1) Goal% Concede% Goal% Game #2 Win (1) Goal% Goal% Concede% Game #3 Lose (0) Goal% Concede% Concede% Game #n Win (1) Concede% Goal% Goal% Goal%
  67. 67. aggregation by sequence with outcome Outflow (Careflow) overview / summary Event Sequences! with Outcome!
  68. 68. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1
  69. 69. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1%
  70. 70. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1% e2% e2%
  71. 71. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1% e2% e2% e3%
  72. 72. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1% [e1] e2% e2% e3% States [e1, e2] [e1, e2, e3]
  73. 73. Select alignment point Pick a state What are the paths What are the paths that led to ? after ? Example Soccer: Goal, Concede, Goal
  74. 74. Outflow Graph Alignment Point [e1, e2, e3]!
  75. 75. 1%record% Outflow Graph Alignment Point [e1]! [e1, e2]! [ ]! [e1, e2, e3]! [e1, e2, e3, e5]!
  76. 76. 2%records% Outflow Graph Alignment Point [e1]! [e1, e2]! [ ]! [e1, e3]! [e1, e2, e3]! [e1, e2, e3, e5]!
  77. 77. 3%records% Outflow Graph Alignment Point [e1]! [e1, e2]! [e1, e2, e3, e4]! [ ]! [e1, e3]! [e1, e2, e3]! [e1, e2, e3, e5]! [e3]!
  78. 78. n%records% Outflow Graph Alignment Point [e1]! [e1, e2]! [e1, e2, e3, e4]! [ ]! [e2]! [e1, e3]! [e1, e2, e3]! [e1, e2, e3, e5]! [e3]! [e2, e3]!
  79. 79. n%records% Outflow Graph Alignment Point [e1]! [e1, e2]! [e1, e2, e3, e4]! [ ]! [e2]! [e1, e3]! [e1, e2, e3]! [e1, e2, e3, e5]! [e3]! [e2, e3]! Average outcome = 0.4 Average time = 10 days No. of records = 10
  80. 80. Soccer Results Alignment Point 1-0! 2-0! 2-2! 0-0! 1-1! 2-1! 3-1! 0-1! 0-2!
  81. 81. Past& Future& Alignment% Node’s horizontal position shows sequence of states.% e1! e2! e3! End of path% e1! e1! e2! 7me% link% e1! Node’s height is edge% edge% e2! number of records.% e4! e2! Color is outcome Time edge’s width is measure.% duration of transition.%
  82. 82. Wongsuphasawat, K., & Gotz, D. (2012). Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2659–2668.
  83. 83. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group + Outcome
  84. 84. Application to Big Data Analysis
  85. 85. Something sounds simple X magnitude of big data = Big mess & Big reward
  86. 86. Event Sequence Analysis at eBay CheckoutProcStep1 PaymentReview CheckoutProcStep2 CheckoutProcStep3 PaymentConfirm CheckoutProcStep4 CheckoutProcStep5 CheckoutProcStep6 CheckoutSuccess
  87. 87. eBay Event Sequence Analysis at alignment Shen, Z., Wei, J., Sundaresan, N., & Ma, K.-L. (2012). Visual analysis of massive web session data. IEEE Symposium on Large Data Analysis and Visualization (LDAV), 65–72.
  88. 88. Event Sequence Analysis at Twitter •  Data –  TBs of session logs everyday •  Complexity –  millions of sessions per day –  1000+ types of events –  long sessions •  Goal –  Overview of how users are using Twitter •  Technique –  LifeFlow Simplify!
  89. 89. Event Sequence Analysis at Twitter (2) •  So far –  millions of sessions per day –  millions of sessions on the same screen –  1000+ types of events –  simplified sets of events •  e.g., pages only, selected pages only –  long sessions –  limited session length to 10-20 events
  90. 90. Event Sequence Analysis at Twitter (3) Session%Start% Page%A% Page%B% Page%C% Page%B% Page%A% Page%D% Page%C% Page%D% Page%B% Page%C% Page%D% Page%C% *fake data
  91. 91. Event Sequence Analysis at Twitter (4) •  Implementation –  Hadoop  –  Web-based (js) •  More –  Stored preprocessed data in smaller db (MySQL/Vertica) Interactive MySQL / HDFS Vertica Visualization Batch pig scripts
  92. 92. Takeaway Messages •  Life is full of event sequences. •  How to visualize an event sequence Krist Wongsuphasawat krist.wongz@gmail.com @kristw
  93. 93. linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
  94. 94. Takeaway Messages •  Life is full of event sequences. •  How to visualize an event sequence •  How to visualize collection of event seq. Krist Wongsuphasawat krist.wongz@gmail.com @kristw
  95. 95. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group + Outcome
  96. 96. Takeaway Messages •  Life is full of event sequences. •  How to visualize an event sequence •  How to visualize collection of event seq. •  Applicable to big data •  New techniques happen everyday. Krist Wongsuphasawat krist.wongz@gmail.com @kristw
  97. 97. Smurf Communism - Wikipedia delete keep … http://notabilia.net/
  98. 98. http://www.evolutionoftheweb.com
  99. 99. Takeaway Messages •  Life is full of event sequences. •  How to visualize an event sequence •  How to visualize collection of event seq. •  Applicable to big data •  New techniques happen everyday. Krist Wongsuphasawat krist.wongz@gmail.com @kristw

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