Progressive Visual Analytics!

Transcription

1 Progressive Visual Analytics! User-Driven Visual Exploration! of In-Progress Analytics Chad Stolper Georgia Tech Adam Perer IBM T.J. Watson Research Center David Gotz UNC Chapel Hill

2 Visual Analytics Workflow 2

3 Visual Analytics Workflow 3

4 Visual Analytics Workflow 4

5 Visual Analytics Workflow 5

6 Visual Analytics Workflow 6

7 Visual Analytics Workflow 7

8 Visual Analytics Workflow 8

9 Visual Analytics Workflow 9

10 Visual Analytics Workflow Bad! 10

11 Visual Analytics Workflow Bad! 11

12 Visual Analytics Workflow Very Bad! 12

13 1. Increasingly large quantities of data Increasingly complex analytic algorithms 13

14 1. Increasingly large quantities of data 2. Increasingly complex analytics 14

15 1. Increasingly large quantities of data 2. Increasingly complex analytics 15

16 Batch Visual Analytics Workflow 16

17 Batch Visual Analytics Workflow 17

18 Batch Visual Analytics Workflow 18

19 Batch Visual Analytics Workflow 19

20 Batch Visual Analytics Workflow 20

21 Batch Visual Analytics Workflow Progressive 21

22 Batch Visual Analytics Workflow Progressive 22 D. Fisher, I. Popov, S. Drucker, and m. c. schraefel, Trust me, i m partially right: incremental visualization lets analysts explore large datasets faster, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA, 2012, pp

23 Batch Visual Analytics Workflow Progressive 23

24 Batch Visual Analytics Workflow Progressive When is this strategy viable? How do we design systems to take advantage of it? 24

25 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 25

26 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 26

27 Progressive Analytics Semantically-Meaningful Partial Results 27

28 Progressive Analytics Semantically-Meaningful Partial Results (Thomas-- Results Feedback) 28

29 Progressive Analytics Semantically-Meaningful Partial Results The partial results are of the same form as the final results 29

30 Progressive Analytics Semantically-Meaningful Partial Results 30

31 Progressive Analytics Semantically-Meaningful Partial Results 31

32 Progressive Analytics Semantically-Meaningful Partial Results 32

33 Progressive Analytics Semantically-Meaningful Partial Results 33

34 Progressive Analytics Semantically-Meaningful Partial Results K-Means Logistic Regression 34

35 Progressive Analytics Semantically-Meaningful Partial Results K-Means Logistic Regression 35

36 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 36

37 User-Driven Analytics Incorporate Analyst Knowledge 37

38 User-Driven Analytics Incorporate Analyst Knowledge 38

39 User-Driven Analytics Incorporate Analyst Knowledge 39

40 User-Driven Analytics Incorporate Analyst Knowledge (Thomas Result Control) 40

41 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 41

42 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 42

43 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 43

44 Progressive Visualization Visualize the Partial Results 44

45 Progressive Visualization Visualize the Partial Results (Thomas Results Feedback) 45

46 Progressive Visualization Analytic Visualization Analyst 46

47 Progressive Visualization Up-to-Date Information Analytic Visualization Analyst 47

48 Progressive Visualization Up-to-Date Information Analytic Visualization Analyst 48

49 Progressive Visualization Up-to-Date Information Analytic Visualization Analyst 49

50 Progressive Visualization Up-to-Date Information Analytic Visualization Analyst

51 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 51

52 Progressive Visualization Up-to-Date Information Analytic Visualization Analyst 52

53 Interactive Visualization Up-to-Date Information Analytic Visualization Analyst Analyst s Domain Knowledge 53

54 Interactive Visualization Up-to-Date Information Analytic Visualization Analyst Analyst s Domain Knowledge 54 (Thomas Execution Control)

55 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 55

56 56 So what might such a system look like?

57 Electronic Medical Records 57

58 Electronic Medical Records Procedures Lab Tests Diagnoses Medications 58

59 Electronic Medical Records HF BB HF D Frequent Patterns Event Series Correlate with Outcomes 59

60 Electronic Medical Records HF D HF D HF BB HF D COPD HF D HF BB BB D Frequent Patterns Event Series Correlate with Outcomes 60

61 Electronic Medical Records HF D HF D HF BB HF D COPD HF D HF BB BB D Frequent Patterns Event Series Correlate with Outcomes 61

62 Electronic Medical Records HF D HF D HF BB HF D COPD HF D HF BB BB D Frequent Patterns Event Series Correlate with Outcomes 62

63 Electronic Medical Records HF D HF D HF BB HF D COPD HF D HF BB BB D Frequent Patterns Event Series Correlate with Outcomes 63

64 Electronic Medical Records HF D HF D HF BB HF D COPD HF D What Works? (What Doesn t Work?) HF BB BB D Frequent Patterns Event Series Correlate with Outcomes 64

65 Electronic Medical Records HF D HF D HF BB HF D COPD HF D What Works? (What Doesn t Work?) HF BB BB D Frequent Patterns Event Series Correlate with Outcomes 65

66 Sequential Pattern Mining Algorithm 66 J.Ayres, J.Flannick, J.Gehrke, and T.Yiu. Sequential Pattern mining using a bitmap representation. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 02, pages , New York, NY, USA, ACM.

67 Sequential Pattern Mining Algorithm (Thomas Dependent Subdivision) 67 J.Ayres, J.Flannick, J.Gehrke, and T.Yiu. Sequential Pattern mining using a bitmap representation. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 02, pages , New York, NY, USA, ACM.

68 Sequential Pattern Mining Algorithm Build a tree of every possible pattern (up to a maximum length) Prune the tree (using a support threshold) 68

69 Sequential Pattern Mining Algorithm COPD 50% Support (2 Patients) HF D BB D HF BB HF D HF BB HF BB HF Build a tree of every possible pattern (up to a maximum length) Prune the tree (using a support threshold) HF BB D BB 69

70 Sequential Pattern Mining Algorithm COPD 50% Support (2 Patients) HF D BB D HF BB HF D HF BB HF BB HF Build a tree of every possible pattern (up to a maximum length) Prune the tree (using a support threshold) HF BB D BB 70

71 Sequential Pattern Mining Algorithm COPD 50% Support (2 Patients) HF D BB D HF BB HF D HF BB HF BB HF Build a tree of every possible pattern (up to a maximum length) Prune the tree (using a support threshold) HF BB D BB 71

72 Sequential Pattern Mining Algorithm Frequent Patterns Event Series Correlate with Outcomes Depth-First Search Outputs frequent patterns as each is found 72

73 Sequential Pattern Mining Algorithm Frequent Patterns Event Series Correlate with Outcomes Depth-First Search Outputs frequent patterns as each is found 73

74 Sequential Pattern Mining Algorithm Frequent Patterns Event Series Correlate with Outcomes Depth-First Search Outputs frequent patterns as each is found 74

75 Interactive Sequential Pattern Mining Frequent Patterns Event Series Correlate with Outcomes Breadth-First Search Shorter patterns first Analyst prioritization Outputs frequent patterns as each is found Manual Pruning 75

76 Interactive Sequential Pattern Mining Frequent Patterns Event Series Correlate with Outcomes Breadth-First Search Shorter patterns first Analyst prioritization Outputs frequent patterns as each is found Manual Pruning HF BB D COPD BB D 76

77 Interactive Sequential Pattern Mining Frequent Patterns Event Series Correlate with Outcomes Breadth-First Search Shorter patterns first Analyst prioritization Outputs frequent patterns as each is found Manual Pruning HF BB D 77 COPD

78 Interactive Sequential Pattern Mining Breadth-First Search Shorter patterns first Analyst prioritization Outputs frequent patterns as each is found Manual Pruning 78

79 Interactive Sequential Pattern Mining Breadth-First Search Shorter patterns first Analyst prioritization Outputs frequent patterns as each is found Manual Pruning 79

80 Interactive Sequential Pattern Mining HF BB D COPD Breadth-First Search Shorter patterns first Analyst prioritization Outputs frequent patterns as each is found Manual Pruning 80

81 Interactive Sequential Pattern Mining Breadth-First Search Shorter patterns first Analyst prioritization Outputs frequent patterns as each is found Manual Pruning 81

82 Interactive Sequential Pattern Mining Breadth-First Search Shorter patterns first Analyst prioritization COPD D Outputs frequent patterns as each is found Manual Pruning 82

83 Interactive Sequential Pattern Mining Breadth-First Search Shorter patterns first Analyst prioritization COPD D Outputs frequent patterns as each is found Manual Pruning 83

84 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 84

85 Progressive Visual Analytics Progressive, User-Driven Analytics Progressive, Interactive Visualization 85

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87 HF COPD D 87

88 HF COPD 88

89 HF 89

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120 ?? 120

121 ?? 121

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128 128 So Does it Work?

129 So Does it Work? Medical Researchers, University Hospital of North Norway 129

130 So Does it Work? AMIA 2014 Skrovseth, Perer, Delaney, Revaug, Lindsetmo, Augestad "Detecting Novel Associations for Surgical Hospital Readmissions in Large Datasets by Interactive Visual Analytics J 130

131 Contributions A precise definition and design guidelines for Progressive Visual Analytics An example Progressive Visual Analytics system, Progressive Insights (In the paper) A case study of using Progressive Insights for exploring frequent patterns in EHR 131

132 Contributions A precise definition and design guidelines for Progressive Visual Analytics An example Progressive Visual Analytics system, Progressive Insights (In the paper) A case study of using Progressive Insights for exploring frequent patterns in EHR 132

133 Contributions A precise definition and design guidelines for Progressive Visual Analytics An example Progressive Visual Analytics system, Progressive Insights (In the paper) A case study of using Progressive Insights for exploring frequent patterns in EHR 133

134 Contributions A precise definition and design guidelines for Progressive Visual Analytics An example Progressive Visual Analytics system, Progressive Insights (In the paper) A case study of using Progressive Insights for exploring frequent patterns in EHR 134

135 Chad Stolper Georgia Tech Adam Perer IBM T.J. Watson Research Center David Gotz UNC Chapel Hill 135

136 Thank You! Chad Stolper Georgia Tech Adam Perer IBM T.J. Watson Research Center David Gotz UNC Chapel Hill 136

137 Questions? Chad Stolper Georgia Tech Adam Perer IBM T.J. Watson Research Center David Gotz UNC Chapel Hill 137

138 Questions? Chad Stolper Georgia Tech Adam Perer IBM T.J. Watson Research Center David Gotz UNC Chapel Hill 138

139 Progressive Visual Analytics Seven Design Guidelines 139

140 Progressive Visual Analytics Systems Analytics should 1. Provide increasingly meaningful partial results as the algorithm executes 2. Allow users to focus the algorithm to subspaces of interest 3. Allow users to ignore irrelevant subspaces 140

141 Progressive Visual Analytics Systems Visualizations should 4. Minimize distractions by not changing views excessively 5. Provide cues to indicate where new results have been found 6. Support on-demand refresh 7. Provide an interface to specify where the analytics should focus and ignore 141