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

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

  1. 1. event event event event event event event event LIFE event event event event event event event event
  2. 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. 3. event event event event event event event event LIFE event event event event event event event event “Event Sequence”
  4. 4. Daily Activities 7:00/W!"# $p 7:15/S%&w#r 8:00/Br#!"f!'(
  5. 5. Student Progress A$)’07/E*(#r M!+’09/M!'(#r Apr’12/D#f#*'#
  6. 6. Event Sequences Medical Transportation Sports Education Web logs Logistics and more…
  7. 7. Two interesting problems
  8. 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. 9. 2. Approximate search ICU Floor ICU QUERY within 2 days Find something useful and display. RESULTS Frustrated! Found 0 record
  10. 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. 11. Outline Approximate Introduction Search Conclusions LifeFlow Case Studies Overview How to provide an overview of multiple event sequences?
  12. 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. 13. To multiple event sequences... •  Search [Fails06], [Wang08], [Vrotsou09], …
  14. 14. To multiple event sequences... •  Search [Fails06], [Wang08], [Vrotsou09], … •  Group [Phan07], [Burch08], [Wang09], … 1 { 2 {
  15. 15. but…
  16. 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. 17. Overview / Summary Millions of records!
  18. 18. Challenges Squeeze into one screen AGGREGATE Screen Millions of records Preserve information!
  19. 19. 1 # LifeFlow scalable & novel overview summarizes all possible sequences! & gaps between events!
  20. 20. Demo LifeFlow Design
  21. 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. 22. Demo LifeFlow
  23. 23. User Study xxxxx 12-minute yyyyy 10 participants training 15 tasks Participants could perform the tasks accurately and rapidly.
  24. 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. 25. wait for the case studies :)
  26. 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. 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. 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. 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. 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. 31. 2 # Similarity Search Similarity Measure Match & Mismatch + User Interface Similan What is similar?! Specify query / Display results! Version 1 xxxxyyyy Version 2
  32. 32. Screenshot Similan
  33. 33. Controlled Experiment Exact Match Similarity Search LifeLines2 Similan xxxxxxxxx xxxxyyyyy 18 participants
  34. 34. Lessons Exact Match Similarity Search Counting Similar Confidence Flexible Uncertainty accept reject
  35. 35. Combination Exact Match + Similarity Search = Hybrid accept reject accept reject
  36. 36. 3 # Flexible Temporal Search (FTS) “mandatory” Results Begin Query Constraint #1 PASS FAIL Constraint #2 accept Constraint #3 mandatory reject optional Reject
  37. 37. 3 # Flexible Temporal Search (FTS) “optional” Results Query Constraint #1 PASS FAIL Constraint #2 accept Constraint #3 mandatory reject optional
  38. 38. mandatory Constraints •  Event A! B! C! Aug 14, 2000 •  Timing A! •  Negation A! C! B! •  Gap A! 1-2 days! C!
  39. 39. optional Constraints •  Event A! B! C! Aug 14, 2000 •  Timing A! •  Negation A! C! B! •  Gap A! 1-2 days! C!
  40. 40. FTS Matching Time Query A! B! C! D! E! Record #2 A! B! D! C!
  41. 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. 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. 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. 44. Demo Flexible Temporal Search (FTS)
  45. 45. Outline Approximate Introduction Search Conclusions LifeFlow Case Studies Overview Multi-dimensional In-depth Long-term Case Studies (MILCs)
  46. 46. “to the wild”
  47. 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. 48. Case #1: Medical User: Dr. A. Zach Hettinger MedStar Institute for Innovation mi2.org Data: 60,041 patients Task: Hospital readmissions
  49. 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. 50. 60,041 patients How many patients came back? Did they come back for the 3rd, 4th, … time? Registration
  51. 51. 60,041 patients Registration How many came back and died? Death
  52. 52. 60,041 patients Location Registration Admission Death
  53. 53. 60,041 patients Find a pattern: Registration > Discharge > Registration > Death Registration Discharge Death
  54. 54. 60,041 patients Find a pattern: Registration > Discharge > Registration > Death Registration Discharge Death
  55. 55. Analyzing data in a new way Personal exploration Long-term monitoring Save more lives!
  56. 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. 57. 100 Years!
  58. 58. Clean the data!
  59. 59. Video
  60. 60. Suspicious distribution!
  61. 61. Detect anomalies Clean data Large dataset
  62. 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. 63. ~5 MINUTES
  64. 64. 24 SECONDS
  65. 65. Understand data Surprising pattern New hypotheses
  66. 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. 67. Find interesting matches Begin Score Opponent Score End
  68. 68. Demolish another team.
  69. 69. Came back after conceded two goals.
  70. 70. Performance: home vs. away Begin Score Opponent Score Missed Penalty End
  71. 71. Finding specific situations. Begin Score Opponent Score Missed Penalty End
  72. 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. 73. Outline Approximate Introduction Search Conclusions LifeFlow Case Studies Overview
  74. 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. 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. 76. Outline Approximate Introduction Search Conclusions LifeFlow Case Studies Overview
  77. 77. Outline Approximate Introduction Search Conclusions LifeFlow Case Studies Overview This is an event sequence!
  78. 78. refresh
  79. 79. fruitful
  80. 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. 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. 82. Thank you ขอบคุณครับ

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