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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.
Citation preview
Visualizations for Event Sequences Exploration
Krist Wongsuphasawat
Data Visualization Scientist Twitter, Inc.
@kristw
Data Visualization Summit San Francisco, CA
Apr 11, 2013
Life event%
event%event%
event%event%
event%event%event%
event%
event%
event%
event%
event%
event% event%event%
( 7:00 am, Wake up )
Life event%
event%event%
event%event%
event%event%event%
event%
event%
event%
event%
event%
event% event%event%
Time Event type%
Life event%
event%event%
event%event%
event%event%event%
event%
event%
event%
event%
event%
event% event%event%
“Event Sequence”
Daily Activity
7:30 a.m. Wake Up
7:45 a.m. Exercise
8:30 a.m. Go to work
Traffic Incidents
9:30 a.m. Notification
9:55 a.m. Units arrived
10:30 a.m. Road cleared
http://timeline.national911memorial.org/
Event Sequences
and more…
Medical Transportation
Education
Web logs
Sports
Logistics
Outline
What are event sequences?
How to visualize them?
Apply to big data
Visualization Techniques
Event sequence
glyphs timeline
http://timeline.verite.co/
simple event sequence timeline.js
Horizontal axis = time
Glyphs = events
Event sequence
glyphs timeline
Interval +
interval
• Car crash (point) 10 a.m.
time
• Meeting (interval) 10 – 11 a.m.
CATT Lab, University of Maryland -- http://teachamerica.com/VIZ11/VIZ1102Pack/index.htm
interval >> width tra!c incident
http://stoicloofah.github.io/chronoline.js/
interval >> width chronoline.js
Event sequence
glyphs timeline
Interval +
Event types
+
width
types
time
Nurses’ actions Doctors’ actions
They all look similar.
types
time
Nurses’ actions Doctors’ actions
Better?
http
://w
ww
.gua
rdia
n.co
.uk/
wo
rld
/int
erac
tive
/20
11/m
ar/2
2/m
idd
le-e
ast-
pro
test
-int
erac
tive
-tim
elin
e
types >> color The path of protest
http
://ti
meg
lider
.co
m/w
idg
et/
types >> colors + shapes timeglider.js
Event sequence
glyphs timeline
Interval +
Event types
+
High density
+
colors shapes
width
high density
time
Too many overlaps and occlusions
Google Chrome > Developer Tools > Timeline
high density >> facet Google Chrome
scripting rendering & painting
Facet
loading
http://www.cs.umd.edu/lifelines
high density >> facet 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
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.
high density >> aggregation CloudLines (2)
Event sequence
glyphs timeline
Interval +
Event types
+
High density
+
colors shapes
width
facet aggregation
linear
non-linear
binning
circular timeline
2008 2009 2010 2011 2012
linear
repeating patterns circular
Jan Feb
Mar
Apr
May
Jun Jul
Aug
Sep
Oct
Nov
Dec
VanDaniker, M. (2010). Leverage of Spiral Graph for Transportation System Data Visualization. Transportation Research Record: Journal of the Transportation Research Board, 2165, 79–88.
circular timeline (2) Tra!c Incidents
stacked timeline
2008
2009
2010
2011
2012
2008 2009 2010 2011 2012
linear
200
8
200
9
2010
2011
2012
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).
stacked timeline (2) Tweet Volume
Event sequence
glyphs timeline
Interval +
Event types
+
High density
+
colors shapes
width
facet aggregation
linear
non-linear
binning
Event sequence
Event sequence
Event sequence
...
1 2 n collection
collection multiple timelines
Event sequence #1
Event sequence #2
Event sequence #3
Event sequence #4
Event sequence
Event sequence
Event sequence
...
1 2 n collection
Millions!
Event sequence
Event sequence
Event sequence
...
1 2 n
Interactions
collection
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
Group by sequence length
1
2
3
or any clustering algorithm / properties
Interaction #5
search • Simple search – Sequence matching – Subsequence matching
• Regular Expression
ABC
A B* (C|D)
AABCDEFGH AXAYBZCED
AB C 75% D 25%
X 50% Y 50%
Interaction #5
search (2) • Dynamic
ABC D 50% E 50%
X 70% Y 30%
Interaction #5
search (2) • Dynamic
• Similarity search ABCD Similar to
ABCD ABD ACE …
Event sequence
Interactions
search
Event sequence
Event sequence
...
1 2 n
Aggregation
rank
filter group
align by
time
collection
aggregation by time
temporal summary
bin & count
Day 1 Day 2 Day 3 Day 4 Day 5
Wan
g, T
. D.,
Pla
isan
t, C
., S
hnei
der
man
, B.,
Sp
ring
, N.,
Ro
sem
an, D
., M
arch
and
, G.,
Muk
herj
ee, V
., et
al.
(20
09
).
Tem
po
ral S
umm
arie
s: S
upp
ort
ing
Tem
po
ral C
ateg
ori
cal S
earc
hing
, Ag
gre
gat
ion
and
Co
mp
aris
on.
IE
EE
Tra
nsac
tio
ns o
n V
isua
lizat
ion
and
Co
mp
uter
Gra
phi
cs, 1
5(6
), 10
49
–10
56.
aggregation by time
temporal summary
Event sequence
Interactions Aggregation
search
Event sequence
Event sequence
...
1 2 n
rank
filter group
align by
time
by sequence
collection
aggregation by sequence
LifeFlow
e.g. 1) What happened to the patients after they arrived?
Arrival!
ICU!
?
? ? ? ?
?
2) What happened to the patients before & after ICU?
aggregation by sequence
LifeFlow
Millions of records!
overview / summary
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
contact!
home!
profile!
home!
home!
start! photos! home!
http://www.google.com/analytics
aggregation by sequence
Google Analytics
contact!
home!
profile!
start! photos! home!
http://www.google.com/analytics
aggregation by sequence
Google Analytics
contact!
home!
profile!
start! photos!
home!
videos!
http://www.google.com/analytics
aggregation by sequence
Google Analytics
top pages only
height = number of visits
Event sequence Outcome +
Game #1
Time%
10th minute Goal
90th minute Goal
25th minute Concede
Win (1)
or any sports
Game #1
Game #2
Time%
Game #3
Game #n
Lose (0)
Win (1)
Win (1)
Win (1)
Goal% Concede% Goal%
Goal% Goal% Concede%
Goal% Concede%Concede%
Concede% Goal%Goal%Goal%
aggregation by sequence with outcome
Outflow (Careflow) overview / summary
Event Sequences!with Outcome!
Assumption
e1%
Record #1
e2% e3%
Record #1
Events are persistent.
Assumption
e1%
Record #1
e2% e3%
e1%
Record #1
e1% e1%
Events are persistent.
Assumption
e1%
Record #1
e2% e3%
e1%
Record #1
e1%e2%
e1%e2%
Events are persistent.
Assumption
e1%
Record #1
e2% e3%
e1%
Record #1
e1%e2%
e1%e2%e3%
Events are persistent.
Assumption
e1%
Record #1
e2% e3%
e1%
Record #1
e1%e2%
e1%e2%e3%
[e1]
[e1, e2]
[e1, e2, e3] States
Events are persistent.
Select alignment point
Pick a state
What are the paths that led to ?
What are the paths after ?
Soccer: Goal, Concede, Goal
Example
Outflow Graph
[e1, e2, e3]!
Alignment Point
Outflow Graph
[e1, e2, e3]!
[e1, e2]!
[e1, e2, e3, e5]!
[e1]!
[ ]!
Alignment Point
1%record%
Outflow Graph
[e1, e3]!
Alignment Point
2%records%
[e1, e2, e3]!
[e1, e2]!
[e1, e2, e3, e5]!
[e1]!
[ ]!
Outflow Graph
[e1, e2, e3, e4]!
Alignment Point
[e3]!
3%records%
[e1, e3]!
[e1, e2, e3]!
[e1, e2]!
[e1, e2, e3, e5]!
[e1]!
[ ]!
Outflow Graph
[e2, e3]!
[e2]!
Alignment Point
n%records%
[e1, e2, e3, e4]!
[e3]!
[e1, e3]!
[e1, e2, e3]!
[e1, e2]!
[e1, e2, e3, e5]!
[e1]!
[ ]!
Outflow Graph
[e2, e3]!
[e2]!
Alignment Point
n%records%
Average outcome Average time No. of records
= 0.4 = 10 days = 10
[e1, e2, e3, e4]!
[e3]!
[e1, e3]!
[e1, e2, e3]!
[e1, e2]!
[e1, e2, e3, e5]!
[e1]!
[ ]!
Soccer Results
2-1!
2-0!
1-1!
0-2!
2-2!
3-1!
1-0!
0-1!
0-0!
Alignment Point
Alignment%Future&Past&
e1!e2!
e1!
e2!
e1!e2!e3!
e1!e2!e4!
Color is outcome measure.%
Node’s height is number of records.%
Time edge’s width is duration of transition.%
Node’s horizontal position shows sequence of states.%
7me%edge%
link%edge%
End of path%
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.
Event sequence
Interactions Aggregation
search
Event sequence
Event sequence
...
1 2 n
rank
filter group
align by
time
by sequence
collection
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
She
n, Z
., W
ei, J
., S
und
ares
an, N
., &
Ma,
K.-
L. (
2012
).
Vis
ual a
naly
sis
of
mas
sive
web
ses
sio
n d
ata.
IE
EE
Sym
po
sium
on
Larg
e D
ata
Ana
lysi
s an
d V
isua
lizat
ion
(LD
AV
), 6
5–72
.
Event Sequence Analysis at
eBay alignment
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%C%
Page%D%
Page%B%
Page%D%
*fake data
Page%C%Page%D%
Page%C%
Event Sequence Analysis at
Twitter (4) • Implementation
– Hadoop – Web-based (js)
• More – Stored preprocessed data in smaller db
(MySQL/Vertica)
HDFS MySQL / Vertica
Batch pig scripts
Visualization
Interactive
Krist Wongsuphasawat [email protected]
@kristw
• Life is full of event sequences.
• How to visualize an event sequence
Takeaway Messages
Event sequence
glyphs timeline
Interval +
Event types
+
High density
+
colors shapes
width
facet aggregation
linear
non-linear
binning
Krist Wongsuphasawat [email protected]
@kristw
• Life is full of event sequences.
• How to visualize an event sequence
• How to visualize collection of event seq.
Takeaway Messages
Event sequence
Interactions Aggregation
search
Event sequence
Event sequence
...
1 2 n
rank
filter group
align by
time
by sequence
collection
Outcome +
Krist Wongsuphasawat [email protected]
@kristw
• 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.
Takeaway Messages
delete keep
http://notabilia.net/
…
Smurf Communism - Wikipedia
http://www.evolutionoftheweb.com
Krist Wongsuphasawat [email protected]
@kristw
• 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.
Takeaway Messages