Krist Wongsuphasawat's Dissertation Defense: Interactive Exploration of Temporal Event Sequences

1. event event event event event event event event LIFE event event event event event event event event

2. 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

3. event event event event event event event event LIFE event event event event event event event event “Event Sequence”

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

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

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

7. Two interesting problems

8. 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

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

10. 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

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

12. 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

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

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

15. but…

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

17. Overview / Summary Millions of records!

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

19. 1 # LifeFlow scalable & novel overview summarizes all possible sequences! & gaps between events!

20. Demo LifeFlow Design

21. 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

22. Demo LifeFlow

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

24. 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? ” ”

25. wait for the case studies :)

26. 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

27. 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]

28. 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

29. 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!

30. 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

31. 2 # Similarity Search Similarity Measure Match & Mismatch + User Interface Similan What is similar?! Specify query / Display results! Version 1 xxxxyyyy Version 2

32. Screenshot Similan

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

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

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

36. 3 # Flexible Temporal Search (FTS) “mandatory” Results Begin Query Constraint #1 PASS FAIL Constraint #2 accept Constraint #3 mandatory reject optional Reject

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

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

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

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

41. 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!

42. 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

43. (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

44. Demo Flexible Temporal Search (FTS)

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

46. “to the wild”

47. 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

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

49. 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? …

50. 60,041 patients How many patients came back? Did they come back for the 3rd, 4th, … time? Registration

51. 60,041 patients Registration How many came back and died? Death

52. 60,041 patients Location Registration Admission Death

53. 60,041 patients Find a pattern: Registration > Discharge > Registration > Death Registration Discharge Death

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

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

56. 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

57. 100 Years!

58. Clean the data!

59. Video

60. Suspicious distribution!

61. Detect anomalies Clean data Large dataset

62. 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 …

63. ~5 MINUTES

64. 24 SECONDS

65. Understand data Surprising pattern New hypotheses

66. 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

67. Find interesting matches Begin Score Opponent Score End

68. Demolish another team.

69. Came back after conceded two goals.

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

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

72. 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

73. Outline Approximate Introduction Search Conclusions LifeFlow Case Studies Overview

74. 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

75. 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, …

76. Outline Approximate Introduction Search Conclusions LifeFlow Case Studies Overview

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

78. refresh

79. fruitful

80. 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

81. 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

82. Thank you ขอบคุณครับ

Krist Wongsuphasawat's Dissertation Defense: Interactive Exploration of Temporal Event Sequences

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