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.
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
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
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.
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
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!
? ?
? ?
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).
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.
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
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
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
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