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.

Published in: Technology, Education

Visualization for Event Sequences Exploration

  1. 1. Data Visualization Summit San Francisco, CA Apr 11, 2013 Visualizations forEvent 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 Activity7:30 a.m. 7:45 a.m. 8:30 a.m.Wake Up Exercise Go to work
  6. 6. Traffic Incidents9:30 a.m. 9:55 a.m. 10:30 a.m.Notification Units arrived Road cleared
  7. 7. http://timeline.national911memorial.org/
  8. 8. Event SequencesMedical TransportationSports EducationWeb 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 timelinesequence
  12. 12. simple event sequencetimeline.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 >> widthtraffic incident CATT Lab, University of Maryland -- http://teachamerica.com/VIZ11/VIZ1102Pack/index.htm
  16. 16. interval >> widthchronoline.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 >> colorhttp://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 >> facetGoogle Chrome loading scripting rendering & painting Facet Google Chrome > Developer Tools > Timeline
  25. 25. high density >> facetLifelines http://www.cs.umd.edu/lifelines
  26. 26. high density >> binningBritish History Timeline bin by year
  27. 27. high density >> aggregationCloudLines Raw event data Kernel Density Estimation + Importance Func. + Truncation Encode cloud size
  28. 28. high density >> aggregationCloudLines (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 ... Eventsequence sequence sequence
  36. 36. collectionmultiple timelines Event sequence #1 Event sequence #2 Event sequence #3 Event sequence #4
  37. 37. collection 1 2 n Event Event ... Eventsequence sequence sequence Millions!
  38. 38. collection 1 2 n Event Event ... Eventsequence sequence sequence Interactions
  39. 39. Interaction #1align
  40. 40. Interaction #1align
  41. 41. Interaction #1align
  42. 42. Interaction #2rank
  43. 43. Interaction #2rank Rank by number of events or any criteria
  44. 44. Interaction #3filter
  45. 45. Interaction #3filter Select only event sequences with events Set your own filters
  46. 46. Interaction #4group
  47. 47. Interaction #4group 1 2 3 Group by sequence length or any clustering algorithm / properties
  48. 48. Interaction #5search •  Simple search ABC –  Sequence matching AABCDEFGH –  Subsequence matching AXAYBZCED •  Regular Expression A B* (C|D)
  49. 49. Interaction #5search (2) •  Dynamic X 50% C 75% AB Y 50% D 25%
  50. 50. Interaction #5search (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 Aggregationalign by time rank search filter group
  52. 52. aggregation by timetemporal summary Day 1 Day 2 Day 3 Day 4 Day 5 bin & count
  53. 53. aggregation by time temporal summaryWang, 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 Aggregationalign by time rank search by sequence filter group
  55. 55. aggregation by sequenceLifeFlow 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 sequenceLifeFlow overview / summary Millions of records!
  57. 57. Demo LifeFlowWongsuphasawat, 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 CHI2011 (pp. 1747–1756).
  58. 58. Demo LifeFlowWongsuphasawat, 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 CHI2011 (pp. 1747–1756).
  59. 59. Demo LifeFlowWongsuphasawat, 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 CHI2011 (pp. 1747–1756).
  60. 60. aggregation by sequenceLifeFlow profile! home! start! home! photos! home! contact! home!
  61. 61. aggregation by sequenceGoogle Analytics profile! start! home! photos! home! contact! http://www.google.com/analytics
  62. 62. aggregation by sequenceGoogle Analytics profile! home! start! home! photos! videos! contact! http://www.google.com/analytics
  63. 63. aggregation by sequenceGoogle Analytics top pages only height = number of visits http://www.google.com/analytics
  64. 64. Event + Outcomesequence
  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 outcomeOutflow (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 stateWhat are the paths What are the pathsthat 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 Aggregationalign by time rank search by sequence filter group + Outcome
  84. 84. Application toBig Data Analysis
  85. 85. Something sounds simple X magnitude of big data = Big mess & Big reward
  86. 86. Event Sequence Analysis ateBay 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 atTwitter •  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 atTwitter (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 atTwitter (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 atTwitter (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 Aggregationalign 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 - Wikipediadelete 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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