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Relaxed Selection Techniques for Querying Time-Series Graphs

Relaxed Selection Techniques for Querying Time-Series Graphs

Time-series graphs are often used to visualize phenomena that change over time. Common tasks include comparing values at different points in time and searching for specified patterns, either exact or approximate. However, tools that support time-series graphs typically separate query specification from the actual search process, allowing users to adapt the level of similarity only after specifying the pattern. We introduce relaxed selection techniques, in which users implicitly define a level of similarity that can vary across the search pattern, while creating a search query with a single-gesture interaction. Users sketch over part of the graph, establishing the level of similarity through either spatial deviations from the graph, or the speed at which they sketch (temporal deviations). In a user study, participants were significantly faster when using our temporally relaxed selection technique than when using traditional techniques. In addition, they achieved significantly higher precision and recall with our spatially relaxed selection technique compared to traditional techniques.

More information on http://www.christianholz.net/relaxed_selection_techniques.html

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Christian Holz

October 06, 2009
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  1. UIST’09 presentation Christian Holz Steven Feiner Relaxed Selection Techniques for

    Querying Time-Series Graphs
  2. .&/0/()1/$(/#) <*:"+(1*)(%$=%>$1:.(*+%?5-*)5*% >$'.1@-"%A)-B*+#-(42%C*D%E$+F2%CE% =*-)*+95#75$'.1@-"7*0.% 3#$-4%/1(!*-$%./!(%/(#($%3*5!*)%*!(1)#"4 ABSTRACT Time-series graphs are often

    used to visualize phenomena that change over time. Common tasks include comparing values at different points in time and searching for speci- fied patterns, either exact or approximate. However, tools that support time-series graphs typically separate query specification from the actual search process, allowing users !"#$%#&&'()*&#+*,(!*&*-$%./ )*!0&$%/1(!*&*-$%./(2($.&*)#/-*! $*3".)#&&'()*&#+*,(!*&*-$%./ !6*$-4*,("#$$*)/! Figure 1: Relaxed selection techniques. Spatially relaxed s between displayed graph and user sketch on a point-by-po are derived from input speed. (Center) In both cases, toler lection. (Right) Tolerant selections allow similarity matches ABSTRACT Time-series graphs are often used to visualize phenomena that change over time. Common tasks include comparing values at different points in time and searching for speci- fied patterns, either exact or approximate. However, tools that support time-series graphs typically separate query specification from the actual search process, allowing users INTRODUCTION Time-series graphs are types of diagram for v time to a single spatial ax naïve viewer to compre time and visually search portions of the graph. W !"#$%#&&'()*&#+*,(!*&*-$%./ )*!0&$%/1(!*&*-$%./(2($.&*)#/-*! $*3".)#&&'()*&#+*,(!*&*-$%./ 3#$-4%/1(!*-$%./!(%/(#($%3*5!*)%*! !6*$-4*,("#$$*)/! Figure 1: Relaxed selection techniques. Spatially relaxed selection (top): Tolerances are between displayed graph and user sketch on a point-by-point basis. Temporally relaxed are derived from input speed. (Center) In both cases, tolerances can be visualized with lection. (Right) Tolerant selections allow similarity matches for patterns that recur in a tim 1 Relaxed Selection Techniques for Querying Time-Series Graphs Christian Holz Steven Feiner mbia University 2008–2009 es and Payments Columbia University 2008–2009 Guide to Fees and Payments Columbia University 2008–2009 Guide to Fees and Payments query matches selection 2 3
  3. time-series graphs 0 15 30 45 60 2006/II 2007/I 2007/II

    2008/I 2008/II 2009/I
  4. time-series graphs

  5. time-series graphs

  6. 0 15 30 45 60 2006/II 2007/I 2007/II 2008/I 2008/II

    2009/I rubber-band selection
  7. 0 15 30 45 60 2006/II Untitled 25 Untitled 32

    Untitled 39 Untitled 46 Untitled 53 Untitled 60 Untitled 67 2008/II Untitled 81 Untitled 88 Untitled 95 Untitled 102 0 15 30 45 60 2006/II 2007/I 2007/II 2008/I 2008/II 2009/I rubber-band selection
  8. 0 15 30 45 60 2006/II Untitled 25 Untitled 32

    Untitled 39 Untitled 46 Untitled 53 Untitled 60 Untitled 67 2008/II Untitled 81 Untitled 88 Untitled 95 Untitled 102 0 15 30 45 60 2006/II 2007/I 2007/II 2008/I 2008/II 2009/I 0 15 30 45 60 2006/II Untitled 25 Untitled 32 Untitled 39 Untitled 46 Untitled 53 Untitled 60 Untitled 67 2008/II Untitled 81 Untitled 88 Untitled 95 Untitled 102 rubber-band selection similarity
  9. relaxed selection techniques 0 15 30 45 60 2006/II 2007/I

    2007/II 2008/I 2008/II 2009/I
  10. relaxed selection techniques

  11. relaxed selection techniques

  12. related work

  13. 0 15 30 45 60 2006/II 2007/I 2007/II 2008/I 2008/II

    2009/I rubber-band selection
  14. 0 15 30 45 60 2006/II Untitled 25 Untitled 32

    Untitled 39 Untitled 46 Untitled 53 Untitled 60 Untitled 67 2008/II Untitled 81 Untitled 88 Untitled 95 Untitled 102 0 15 30 45 60 2006/II 2007/I 2007/II 2008/I 2008/II 2009/I rubber-band selection
  15. 0 15 30 45 60 2006/II Untitled 25 Untitled 32

    Untitled 39 Untitled 46 Untitled 53 Untitled 60 Untitled 67 2008/II Untitled 81 Untitled 88 Untitled 95 Untitled 102 0 15 30 45 60 2006/II 2007/I 2007/II 2008/I 2008/II 2009/I 0 15 30 45 60 2006/II Untitled 25 Untitled 32 Untitled 39 Untitled 46 Untitled 53 Untitled 60 Untitled 67 2008/II Untitled 81 Untitled 88 Untitled 95 Untitled 102 rubber-band selection similarity
  16. TimeSearcher Hochheiser and Shneiderman, Discovery Science’01. Keogh et al., FQAS'02.

    Hochheiser et al. ICME'03. Hochheiser and Shneiderman, Information Visualization’04 Buono et al., VDA’05. Buono and Simeone, AVI’08.
  17. Tableau Spot re

  18. QuerySketch Wattenberg, CHI’01

  19. QuerySketch Wattenberg, CHI’01

  20. QuerySketch Wattenberg, CHI’01

  21. Relevance Feedback Keogh and Pazzani, SIGIR’99 Patterns Morrill, Communic. ACM’98

    VizTree Lin et al., KDD'04 Lin et al., VLDB'04 Ryall et al., CHI’05 QueryLines Line Graph Explorer Kincaid and Lam, AVI’06
  22. relaxed selection techniques

  23. 1. selection

  24. 1. selection

  25. 1. selection 2. level of similarity

  26. 1. selection 2. level of similarity

  27. 1. selection 2. level of similarity 3. noise level

  28. 1. selection 2. level of similarity 3. noise level

  29. how does it work?

  30. ltering 1

  31. ltering 1

  32. pairing 2

  33. pairing 2

  34. pairing 2

  35. pairing unpaired paired 2

  36. two methods to derive the query spatially relaxed selection temporally

    relaxed selection
  37. spatially relaxed selection tolerance tolerance

  38. spatially relaxed selection tolerance tolerance

  39. spatially relaxed selection

  40. spatially relaxed selection end points

  41. spatially relaxed selection end points tolerance

  42. spatially relaxed selection

  43. spatially relaxed selection

  44. spatially relaxed selection

  45. temporally relaxed selection

  46. temporally relaxed selection

  47. temporally relaxed selection

  48. temporally relaxed selection

  49. temporally relaxed selection

  50. temporally relaxed selection

  51. temporally relaxed selection

  52. temporally relaxed selection

  53. matching

  54. matching input 1 user query

  55. matching input 2 time-series graph

  56. matching input 2 time-series graph

  57. matching step 1

  58. matching step 1

  59. matching step 1

  60. matching step 2

  61. matching step 2

  62. matching step 2

  63. matching step 2

  64. matching step 3

  65. matching step 4 angles

  66. user study

  67. techniques spatially relaxed selection SRS temporally relaxed selection TRS rubber-band

    selection RB query-by-example QE
  68. user study 18 subjects age: 18-38

  69. user study 18 subjects age: 18-38 method

  70. user study 18 subjects age: 18-38 select method

  71. user study 18 subjects age: 18-38 select method nd

  72. user study 18 subjects age: 18-38 select method nd advance

  73. design each participant: 4 techniques × 9 datasets × 15

    interactions _________________________ = 540 trials
  74. hypotheses H1. TRS fastest for speci cation. ... H5. SRS

    nds most relevant matches.
  75. results: exact matches !"!!# !"$!# !"%!# !"&!# !"'!# !"(!# !")!#

    !"*!# +,-.#,/-01# precision recall 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % SRS TRS RB QE SRS TRS RB QE
  76. results: exact matches !"!!# !"$!# !"%!# !"&!# !"'!# !"(!# !")!#

    !"*!# +,-.#,/-01# precision recall 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % SRS TRS RB QE SRS TRS RB QE
  77. results: intersecting matches +,-.#123,42# precision recall 0 % 10 %

    20 % 30 % 40 % 50 % 60 % 70 % SRS TRS RB QE SRS TRS RB QE
  78. results: intersecting matches +,-.#123,42# precision recall 0 % 10 %

    20 % 30 % 40 % 50 % 60 % 70 % SRS TRS RB QE SRS TRS RB QE
  79. results: exact matches +,-.#,/-01#5341# precision recall 0 % 10 %

    20 % 30 % 40 % 50 % 60 % 70 % SRS TRS RB QE SRS TRS RB QE rst interaction
  80. results: exact matches +,-.#,/-01#5341# precision recall 0 % 10 %

    20 % 30 % 40 % 50 % 60 % 70 % SRS TRS RB QE SRS TRS RB QE rst interaction
  81. results: intersecting matches +,-.#123,42#5341# precision recall 0 % 10 %

    20 % 30 % 40 % 50 % 60 % 70 % SRS TRS RB QE SRS TRS RB QE rst interaction
  82. results: intersecting matches +,-.#123,42#5341# precision recall 0 % 10 %

    20 % 30 % 40 % 50 % 60 % 70 % SRS TRS RB QE SRS TRS RB QE rst interaction
  83. !" #!!" $!!!" $#!!" %!!!" %#!!" &'(')*+," -./0&12',1" 0 s

    0.5 s 1.0 s 1.5 s 2.0 s results: time selection adjustment SRS TRS RB QE SRS TRS RB QE
  84. !" #!!" $!!!" $#!!" %!!!" %#!!" &'(')*+," -./0&12',1" 0 s

    0.5 s 1.0 s 1.5 s 2.0 s results: time selection adjustment SRS TRS RB QE SRS TRS RB QE
  85. !" #!!" $!!!" $#!!" %!!!" %#!!" &'(')*+," -./0&12',1" 0 s

    0.5 s 1.0 s 1.5 s 2.0 s results: time selection adjustment SRS TRS RB QE SRS TRS RB QE
  86. conclusions 2 new techniques SRS: spatially relaxed selection TRS: temporally

    relaxed selection study: TRS fastest for selection. SRS found the most relevant matches.
  87. Q?