Krist Wongsuphasawat's Dissertation Defense at the University of Maryland, College Park on April 10, 2012

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 Activities
7:00/W!"# $p 7:15/S%&w#r 8:00/Br#!"f!'(

6. Student Progress
A$)’07/E*(#r M!+’09/M!'(#r Apr’12/D#f#*'#

7. Event Sequences
Medical Transportation
Sports Education
Web logs Logistics
and more…

8. Two interesting problems

9. 1. Lack of overview
Show overview
or summary
60,041 patients
203,214 traffic incidents
Where should I start?
Is the dataset cleaned? 7,022 web sessions
… and more

10. 2. Approximate search
ICU Floor ICU
QUERY within 2 days
Find something
useful and display.
RESULTS Frustrated!
Found 0 record

11. Research Questions
Overview Search
How to provide an overview How to support users
of multiple event sequences? when they are uncertain
about what they are looking for?
LifeFlow
Similan
Flexible Temporal Search

12. Outline
Approximate
Introduction Search Conclusions
LifeFlow Case Studies
Overview
How to provide an overview
of multiple event sequences?

13. From one event sequence...
• Single record [Cousins91], [Harrison94], [Plaisant98], …
Patient ID: 45851737
12/02/2008&14:26 &Arrival&
12/02/2008&14:26 &Emergency&
12/02/2008&22:44 &ICU&
12/05/2008&05:07 &Floor&
12/08/2008&10:02 &Floor&
12/14/2008&06:19 &Discharge&
& Time
Patient #45851737 Arrival
Emergency Room
ICU
Floor
Discharge
compact

14. To multiple event sequences...
• Search [Fails06], [Wang08], [Vrotsou09], …

15. To multiple event sequences...
• Search [Fails06], [Wang08], [Vrotsou09], …
• Group [Phan07], [Burch08], [Wang09], …
1 {
2 {

16. but…

17. Summarize
e.g. 1) What happened to the patients after they arrived?
Arrival!
?
?
2) What happened to the patients before & after ICU?
ICU!
? ?
? ?

18. Overview / Summary
Millions of records!

19. Challenges
Squeeze into one screen
AGGREGATE
Screen
Millions of records
Preserve information!

20. 1
#
LifeFlow
scalable & novel overview
summarizes all possible sequences!
& gaps between events!

21. Demo
LifeFlow Design

22. 1
#
time
#1&
Event Sequences
#2&
n records
#3&
…&
1,000,000
Aggregate O(n)
Tree of Sequences
α" No. of patterns
9 nodes
Represent
time
records
LifeFlow
Visual Representation
Space-filling technique
Average time Event Bar End Node

23. Demo
LifeFlow

24. User Study
xxxxx 12-minute
yyyyy
10 participants
training
15 tasks
Participants could perform the tasks
accurately and rapidly.

25. Quotes
“ Oh! This is very cool! ”
“ Theunderstand
easy to
tool is “ LifeFlow provides
a great summary
and easy to use.! ” of the big picture.! ”
“ find common
Very easy to
“ Can I use it
and uncommon
sequences!
with my dataset? ”
”

26. wait for the case studies :)

27. Outline
How to support users when they are uncertain
about what they are looking for?
Approximate
Introduction Search Conclusions
LifeFlow Case Studies
Overview
Similarity Search Hybrid Search

28. Related Work: Exact Match
Exact Match • Event Sequence
MUST have A, B, C – TimeSearcher
[Hochheiser04]
Query – PatternFinder
[Fails06]
– LifeLines2
Record#1
[Wang08]
– ActiviTree
Record#2 [Vrotsou09]
– QueryMarvel
Record#3 [Jin09]

29. Related Work: Similarity Search
• Image Similarity Search
[Kato92] SHOULD have A, B, C
• Stock Price
[Wattenberg01] Query more"
similar!
• Web page
[Watai07] Record#2 0.91
• Bank account
[Chang07] Record#1 0.83
• Event Sequence?
Record#3 0.70

30. Challenges
What is similar?
depends on users/tasks
Query Record #1
A! B! C!
Record #2 missing
A! B! C!
Record #3 extra
A! B! C! D!
Record #4
A! B! time difference C!
Record #5 swap
A! C! B!

31. Match & Mismatch (M&M) Measure
Time
Query Record #1
A! C! B! D!
Record #2
A! B! C! E!
Matched events Missing Extra
}
Time difference
Number of swap Total Score
Number of missing events 0.00-1.00
Number of extra events

32. 2
#
Similarity Search
Similarity Measure
Match & Mismatch + User Interface
Similan
What is similar?! Specify query / Display results!
Version 1
xxxxyyyy
Version 2

33. Screenshot
Similan

34. Controlled Experiment
Exact Match Similarity Search
LifeLines2 Similan
xxxxxxxxx
xxxxyyyyy
18 participants

35. Lessons
Exact Match Similarity Search
Counting Similar
Confidence Flexible
Uncertainty
accept
reject

36. Combination
Exact Match + Similarity Search = Hybrid
accept
reject accept
reject

37. 3
#
Flexible Temporal Search (FTS)
“mandatory”
Results
Begin
Query
Constraint #1 PASS FAIL
Constraint #2 accept
Constraint #3
mandatory reject
optional
Reject

38. 3
#
Flexible Temporal Search (FTS)
“optional”
Results
Query
Constraint #1 PASS FAIL
Constraint #2 accept
Constraint #3
mandatory reject
optional

39. mandatory
Constraints
• Event
A! B! C!
Aug 14, 2000
• Timing
A!
• Negation
A! C!
B!
• Gap
A! 1-2 days! C!

40. optional
Constraints
• Event
A! B! C!
Aug 14, 2000
• Timing
A!
• Negation
A! C!
B!
• Gap
A! 1-2 days! C!

41. FTS Matching
Time
Query
A! B! C! D!
E!
Record #2
A! B! D! C!

42. FTS Matching(2)
i
Query
A! B! C! D! E!
s(0,0) s(1,0)
Record #2
s(0,1)
A!
Dynamic programming
{
B!
j s(i-1, j) + skip( query[i] )
D! s(i, j) = max s(i, j-1) + skip( events[j] )
s(i-1, j-1) + match( query[i], events[j] )
C!

43. Similarity Vector s(i,j)
• No. of matched events (mandatory)
• No. of matched events (optional)
• No. of negations violated (optional)
• No. of negations violated (mandatory)
• No. of time constraints violated
• Time difference
• No. of extra events
– Extra before the first match
– Extra between the first and last match
– Extra after the last match

44. (Flexible Temporal Search)
Query FTS Record#1
Grade Similarity Score
Pass/Fail 0-100
1. Missing events
2. Extra events
3. Negation violations
4. Time difference

45. Demo
Flexible Temporal Search (FTS)

46. Outline
Approximate
Introduction Search Conclusions
LifeFlow Case Studies
Overview
Multi-dimensional In-depth
Long-term Case Studies (MILCs)

47. “to the wild”

48. MILCs
# Domain Data Size Duration
1 Medical 7,041 7 months
2 Transportation 203,214 3 months
3 Medical 20,000 6 months
4 Medical 60,041 1 year
5 Web logs 7,022 6 weeks
6 Activity logs 60 5 months
7 Logistics 821 6 weeks
8 Sports 61 5 weeks
8 case studies / 6 domains

49. Case #1: Medical
User: Dr. A. Zach Hettinger
MedStar Institute for Innovation
mi2.org
Data: 60,041 patients
Task: Hospital readmissions

50. Current Report
Patient Diagnosis Visit Date Physician Visit Date Physician
#1 #1 #2 #2
Mr. X Back pain Jun 10, 2010 Dr. Jones Jun 29, 2010 Dr. Brown
Mr. Y Chest pain Jun 11, 2010 Dr. Jones Jun 20, 2010 Dr. Jones
… … … … … …
An example of current report used in a hospital (fake data)
How many patients came back?
Did they come back for the 3rd, 4th, … time?
How many came back and died?
…

51. 60,041 patients How many patients came back?
Did they come back for the 3rd, 4th, … time?
Registration

52. 60,041 patients
Registration How many came back and died?
Death

53. 60,041 patients
Location
Registration
Admission
Death

54. 60,041 patients Find a pattern:
Registration > Discharge > Registration > Death
Registration
Discharge
Death

55. 60,041 patients Find a pattern:
Registration > Discharge > Registration > Death
Registration
Discharge
Death

56. Analyzing data in a new way
Personal exploration
Long-term monitoring
Save more lives!

57. Case #2: Transportation
User: CATT Lab at the University of Maryland
www.cattlab.umd.edu
Data: 203,214 traffic incidents
Task: Comparing traffic agencies’ performance

58. 100 Years!

59. Clean the data!

60. Video

63. Suspicious distribution!

65. Detect anomalies
Clean data
Large dataset

66. Case #3: Web logs
User: Anne Rose
International Children’s Digital Library
www.childrenslibrary.org
Data: 7,022 sessions
Task: How do people read children books online?
PAGE 1 PAGE 2 PAGE 3 …

69. ~5 MINUTES

70. 24 SECONDS

71. Understand data
Surprising pattern
New hypotheses

72. Case #4: Sports
User: Daniel Lertpratchya
Manchester United soccer fan
www.manutd.com
Data: 61 soccer matches
Task: Find interesting matches to watch replay videos.
Explore data to find fun facts.
Begin Score Opponent Score End

73. Find interesting matches
Begin
Score
Opponent Score
End

74. Demolish another team.

75. Came back after conceded two goals.

76. Performance: home vs. away
Begin
Score
Opponent Score
Missed Penalty
End

77. Finding specific situations.
Begin
Score
Opponent Score
Missed Penalty
End

78. 4
#
Design Guidelines
Align-Rank-Filter Handle event types Incorporate attributes
Breakfast
Lunch } Meal
Multiple levels Multiple overviews Coordinated views
of information
Overview
Record
Event
Search Data preprocessing History / Provenance

79. Outline
Approximate
Introduction Search Conclusions
LifeFlow Case Studies
Overview

80. Contributions
1. How to provide an overview of multiple event sequences?
# 1
LifeFlow Visualization
Aggregation, Visual encodings & Interactions
2. How to support users when they are uncertain about
what they are looking for?
#2 # 3
Similarity Search Hybrid Search
Similan + Match & Mismatch Flexible Temporal Search
4
#
Case Studies + Design Guidelines

81. Future Directions
Outflow
Improve the New tasks:
visualization & UI: comparison,
colors, gaps, … attributes in query, …!
More complex data: Scalability:
stream, interval database,
concurrency, …! cloud computing, …

82. Outline
Approximate
Introduction Search Conclusions
LifeFlow Case Studies
Overview

83. Outline
Approximate
Introduction Search Conclusions
LifeFlow Case Studies
Overview
This is an event sequence!

84. refresh

88. fruitful

89. Acknowledgement
Washington Hospital Center
Dr. A. Zach Hettinger , Dr. Phuong Ho and Dr. Mark Smith
National Institutes of Health
Grant RC1CA147489-02
Center for Integrated Transportation Systems Management
a Tier 1 Transportation Center at the University of Maryland
Study Participants
Advisors, Committees, HCIL Colleagues

90. Contributions
1. How to provide an overview of multiple event sequences?
LifeFlow Visualization
Aggregation, Visual encodings & Interactions
2. How to support users when they are uncertain about
what they are looking for?
Similarity Search Hybrid Search
Similan + Match & Mismatch Flexible Temporal Search
Case Studies + Design Guidelines
http://www.cs.umd.edu/hcil/lifeflow kristw@cs.umd.edu / @kristwongz

91. Thank you
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