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Visualization for Event Sequences Exploration

Krist Wongsuphasawat, profile picture
Krist Wongsuphasawat

The document summarizes Krist Wongsuphasawat's presentation on visualizing event sequences at the 2013 Data Visualization Summit in San Francisco. Wongsuphasawat discussed techniques for visualizing event sequences, including using glyphs on a timeline to represent events, using interval width to represent duration, color and shape to distinguish event types, faceting for high density sequences, and aggregation techniques like binning and kernel density estimation. He demonstrated the LifeFlow tool for providing overviews and summaries of event sequence data. Wongsuphasawat also discussed alignment of sequences, outcome-based aggregation with the Outflow tool, and applications to analyzing big event sequence data like customer checkout processes at eBay.

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Data Visualization Summit San Francisco, CA Apr 11, 2013 Visualizations for Event Sequences Exploration Krist Wongsuphasawat Data Visualization Scientist Twitter, Inc. @kristw
event% event% event% event% event% event% event% event% Life event% event% event% event% event% event% event% event%
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%
event% event% event% event% event% event% event% event% Life event% event% event% event% event% event% event% event% “Event Sequence”
Daily Activity 7:30 a.m. 7:45 a.m. 8:30 a.m. Wake Up Exercise Go to work
Traffic Incidents 9:30 a.m. 9:55 a.m. 10:30 a.m. Notification Units arrived Road cleared
http://timeline.national911memorial.org/
Event Sequences Medical Transportation Sports Education Web logs Logistics and more…
Outline ? u ences e nt seq hat are ev them? W is ualize Ho w to v a b ig dat Ap ply to
Visualization Techniques
Event glyphs timeline sequence
simple event sequence timeline.js Horizontal axis = time Glyphs = events http://timeline.verite.co/
Event glyphs timeline sequence + Interval
interval •  Car crash (point) time 10 a.m. •  Meeting (interval) 10 – 11 a.m.
interval >> width traffic incident CATT Lab, University of Maryland -- http://teachamerica.com/VIZ11/VIZ1102Pack/index.htm
interval >> width chronoline.js http://stoicloofah.github.io/chronoline.js/
Event glyphs timeline sequence + Interval width + Event types
types time Nurses’ actions Doctors’ actions They all look similar.
types time Nurses’ actions Doctors’ actions Better?
The path of protest types >> color http://www.guardian.co.uk/world/interactive/2011/mar/22/middle-east-protest-interactive-timeline
types >> colors + shapes http://timeglider.com/widget/ timeglider.js
Event glyphs timeline sequence + Interval width + Event colors shapes types High + density
high density time Too many overlaps and occlusions
high density >> facet Google Chrome loading scripting rendering & painting Facet Google Chrome > Developer Tools > Timeline
high density >> facet Lifelines http://www.cs.umd.edu/lifelines
high density >> binning British History Timeline bin by year
high density >> aggregation CloudLines Raw event data Kernel Density Estimation + Importance Func. + Truncation Encode cloud size
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.
linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
circular timeline 2008 2009 2010 2011 2012 linear Dec Jan Feb Nov Mar circular Oct Apr repeating patterns Sep May Aug Jun Jul
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.
stacked timeline 2008 2009 2010 2011 2012 linear 2008 2009 2008 2009 2011 2010 2012 2010 2011 2012
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).
linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
collection 1 2 n Event Event ... Event sequence sequence sequence
collection multiple timelines Event sequence #1 Event sequence #2 Event sequence #3 Event sequence #4
collection 1 2 n Event Event ... Event sequence sequence sequence Millions!
collection 1 2 n Event Event ... Event sequence sequence sequence Interactions
Interaction #1 align
Interaction #1 align
Interaction #1 align
Interaction #2 rank
Interaction #2 rank Rank by number of events or any criteria
Interaction #3 filter
Interaction #3 filter Select only event sequences with events Set your own filters
Interaction #4 group
Interaction #4 group 1 2 3 Group by sequence length or any clustering algorithm / properties
Interaction #5 search •  Simple search ABC –  Sequence matching AABCDEFGH –  Subsequence matching AXAYBZCED •  Regular Expression A B* (C|D)
Interaction #5 search (2) •  Dynamic X 50% C 75% AB Y 50% D 25%
Interaction #5 search (2) •  Dynamic X 70% D 50% ABC Y 30% E 50% •  Similarity search Similar to ABCD ABCD ABD ACE …
collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search filter group
aggregation by time temporal summary Day 1 Day 2 Day 3 Day 4 Day 5 bin & count
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.
collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group
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! ? ? ? ?
aggregation by sequence LifeFlow overview / summary Millions of records!
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).
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).
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).
aggregation by sequence LifeFlow profile! home! start! home! photos! home! contact! home!
aggregation by sequence Google Analytics profile! start! home! photos! home! contact! http://www.google.com/analytics
aggregation by sequence Google Analytics profile! home! start! home! photos! videos! contact! http://www.google.com/analytics
aggregation by sequence Google Analytics top pages only height = number of visits http://www.google.com/analytics
Event + Outcome sequence
Time% Game #1 Win (1) 10th minute 25th minute 90th minute Goal Concede Goal or any sports
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%
aggregation by sequence with outcome Outflow (Careflow) overview / summary Event Sequences! with Outcome!
Assumption Events are persistent. Record #1 e1% e2% e3% Record #1
Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1%
Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1% e2% e2%
Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1% e2% e2% e3%
Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1% [e1] e2% e2% e3% States [e1, e2] [e1, e2, e3]
Select alignment point Pick a state What are the paths What are the paths that led to ? after ? Example Soccer: Goal, Concede, Goal
Outflow Graph Alignment Point [e1, e2, e3]!
1%record% Outflow Graph Alignment Point [e1]! [e1, e2]! [ ]! [e1, e2, e3]! [e1, e2, e3, e5]!
2%records% Outflow Graph Alignment Point [e1]! [e1, e2]! [ ]! [e1, e3]! [e1, e2, e3]! [e1, e2, e3, e5]!
3%records% Outflow Graph Alignment Point [e1]! [e1, e2]! [e1, e2, e3, e4]! [ ]! [e1, e3]! [e1, e2, e3]! [e1, e2, e3, e5]! [e3]!
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]!
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
Soccer Results Alignment Point 1-0! 2-0! 2-2! 0-0! 1-1! 2-1! 3-1! 0-1! 0-2!
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.%
Visualization for Event Sequences Exploration
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.
collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group + Outcome
Application to Big Data Analysis
Something sounds simple X magnitude of big data = Big mess & Big reward
Event Sequence Analysis at eBay CheckoutProcStep1 PaymentReview CheckoutProcStep2 CheckoutProcStep3 PaymentConfirm CheckoutProcStep4 CheckoutProcStep5 CheckoutProcStep6 CheckoutSuccess
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.
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!
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
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
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
Takeaway Messages •  Life is full of event sequences. •  How to visualize an event sequence Krist Wongsuphasawat krist.wongz@gmail.com @kristw
linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
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
collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group + Outcome
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
Smurf Communism - Wikipedia delete keep … http://notabilia.net/
http://www.evolutionoftheweb.com
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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Visualization for Event Sequences Exploration

  • 1. Data Visualization Summit San Francisco, CA Apr 11, 2013 Visualizations for Event Sequences Exploration Krist Wongsuphasawat Data Visualization Scientist Twitter, Inc. @kristw
  • 2. event% event% event% event% event% event% event% event% Life event% event% event% event% event% event% event% event%
  • 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. event% event% event% event% event% event% event% event% Life event% event% event% event% event% event% event% event% “Event Sequence”
  • 5. Daily Activity 7:30 a.m. 7:45 a.m. 8:30 a.m. Wake Up Exercise Go to work
  • 6. Traffic Incidents 9:30 a.m. 9:55 a.m. 10:30 a.m. Notification Units arrived Road cleared
  • 8. Event Sequences Medical Transportation Sports Education Web logs Logistics and more…
  • 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
  • 11. Event glyphs timeline sequence
  • 12. simple event sequence timeline.js Horizontal axis = time Glyphs = events http://timeline.verite.co/
  • 13. Event glyphs timeline sequence + Interval
  • 14. interval •  Car crash (point) time 10 a.m. •  Meeting (interval) 10 – 11 a.m.
  • 15. interval >> width traffic incident CATT Lab, University of Maryland -- http://teachamerica.com/VIZ11/VIZ1102Pack/index.htm
  • 16. interval >> width chronoline.js http://stoicloofah.github.io/chronoline.js/
  • 17. Event glyphs timeline sequence + Interval width + Event types
  • 18. types time Nurses’ actions Doctors’ actions They all look similar.
  • 19. types time Nurses’ actions Doctors’ actions Better?
  • 20. The path of protest types >> color http://www.guardian.co.uk/world/interactive/2011/mar/22/middle-east-protest-interactive-timeline
  • 21. types >> colors + shapes http://timeglider.com/widget/ timeglider.js
  • 22. Event glyphs timeline sequence + Interval width + Event colors shapes types High + density
  • 23. high density time Too many overlaps and occlusions
  • 24. high density >> facet Google Chrome loading scripting rendering & painting Facet Google Chrome > Developer Tools > Timeline
  • 25. high density >> facet Lifelines http://www.cs.umd.edu/lifelines
  • 26. high density >> binning British History Timeline bin by year
  • 27. high density >> aggregation CloudLines Raw event data Kernel Density Estimation + Importance Func. + Truncation Encode cloud size
  • 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. linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
  • 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. 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. stacked timeline 2008 2009 2010 2011 2012 linear 2008 2009 2008 2009 2011 2010 2012 2010 2011 2012
  • 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. linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
  • 35. collection 1 2 n Event Event ... Event sequence sequence sequence
  • 36. collection multiple timelines Event sequence #1 Event sequence #2 Event sequence #3 Event sequence #4
  • 37. collection 1 2 n Event Event ... Event sequence sequence sequence Millions!
  • 38. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions
  • 43. Interaction #2 rank Rank by number of events or any criteria
  • 45. Interaction #3 filter Select only event sequences with events Set your own filters
  • 47. Interaction #4 group 1 2 3 Group by sequence length or any clustering algorithm / properties
  • 48. Interaction #5 search •  Simple search ABC –  Sequence matching AABCDEFGH –  Subsequence matching AXAYBZCED •  Regular Expression A B* (C|D)
  • 49. Interaction #5 search (2) •  Dynamic X 50% C 75% AB Y 50% D 25%
  • 50. Interaction #5 search (2) •  Dynamic X 70% D 50% ABC Y 30% E 50% •  Similarity search Similar to ABCD ABCD ABD ACE …
  • 51. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search filter group
  • 52. aggregation by time temporal summary Day 1 Day 2 Day 3 Day 4 Day 5 bin & count
  • 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. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group
  • 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. aggregation by sequence LifeFlow overview / summary Millions of records!
  • 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. 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. 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. aggregation by sequence LifeFlow profile! home! start! home! photos! home! contact! home!
  • 61. aggregation by sequence Google Analytics profile! start! home! photos! home! contact! http://www.google.com/analytics
  • 62. aggregation by sequence Google Analytics profile! home! start! home! photos! videos! contact! http://www.google.com/analytics
  • 63. aggregation by sequence Google Analytics top pages only height = number of visits http://www.google.com/analytics
  • 64. Event + Outcome sequence
  • 65. Time% Game #1 Win (1) 10th minute 25th minute 90th minute Goal Concede Goal or any sports
  • 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. aggregation by sequence with outcome Outflow (Careflow) overview / summary Event Sequences! with Outcome!
  • 68. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1
  • 69. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1%
  • 70. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1% e2% e2%
  • 71. Assumption Events are persistent. Record #1 e1% e2% e3% Record #1 e1% e1% e1% e2% e2% e3%
  • 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. Select alignment point Pick a state What are the paths What are the paths that led to ? after ? Example Soccer: Goal, Concede, Goal
  • 74. Outflow Graph Alignment Point [e1, e2, e3]!
  • 75. 1%record% Outflow Graph Alignment Point [e1]! [e1, e2]! [ ]! [e1, e2, e3]! [e1, e2, e3, e5]!
  • 76. 2%records% Outflow Graph Alignment Point [e1]! [e1, e2]! [ ]! [e1, e3]! [e1, e2, e3]! [e1, e2, e3, e5]!
  • 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. 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. 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. Soccer Results Alignment Point 1-0! 2-0! 2-2! 0-0! 1-1! 2-1! 3-1! 0-1! 0-2!
  • 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.%
  • 83. 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.
  • 84. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group + Outcome
  • 86. Something sounds simple X magnitude of big data = Big mess & Big reward
  • 87. Event Sequence Analysis at eBay CheckoutProcStep1 PaymentReview CheckoutProcStep2 CheckoutProcStep3 PaymentConfirm CheckoutProcStep4 CheckoutProcStep5 CheckoutProcStep6 CheckoutSuccess
  • 88. 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.
  • 89. 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!
  • 90. 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
  • 91. 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
  • 92. 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
  • 93. Takeaway Messages •  Life is full of event sequences. •  How to visualize an event sequence Krist Wongsuphasawat krist.wongz@gmail.com @kristw
  • 94. linear Event glyphs timeline sequence non-linear + Interval width + Event colors shapes types High + facet aggregation binning density
  • 95. 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
  • 96. collection 1 2 n Event Event ... Event sequence sequence sequence Interactions Aggregation align by time rank search by sequence filter group + Outcome
  • 97. 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
  • 98. Smurf Communism - Wikipedia delete keep … http://notabilia.net/
  • 100. 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