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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 ---------------- 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.
Krist WongsuphasawatFollow
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Visualization for Event Sequences Exploration
- 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.%
- 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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