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Tracing Tuples Across Dimensions A Comparison of Scatterplots and Parallel Coordinate Plots

Tracing Tuples Across Dimensions A Comparison of Scatterplots and Parallel Coordinate Plots. Xiaole Kuang (Master student, NUS) Haimo Zhang (PhD student, NUS) Shengdong (Shen) Zhao (Faculty member, NUS) Michael J. McGuffin. (Faculty member,

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Tracing Tuples Across Dimensions A Comparison of Scatterplots and Parallel Coordinate Plots

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  1. Tracing Tuples Across Dimensions A Comparison of Scatterplots and Parallel Coordinate Plots Xiaole Kuang (Master student, NUS) Haimo Zhang (PhD student, NUS)Shengdong (Shen) Zhao (Faculty member, NUS) Michael J. McGuffin (Faculty member, École de technologie supérieure)

  2. Welcome to The Last Talk of TheLast Session of TheLast Day!

  3. of Vienna 9697 km Singapore

  4. Systems, Tools, Interaction Techniques Vignette (CHI ‘12) SandCanvas (CHI ‘11) MOGCLASS (CHI ‘11) Magic Cards (CHI ‘09) Zone & Polygon Menu (CHI ‘06) Elastic Hierarchy (InfoVis ‘05) Simple Marking Menu (UIST ‘04) earPod (CHI ‘07)

  5. Visualization Techniques for Multi-Variate Data Parallel Coordinate Plot (PCP) Scatter Plot Matrix (SPLOM) Scatter Plot (SCP)

  6. Why PCP vs. SCP? Viau et al., TVGC10 We need more systematic evaluations between PCP & SCP! Yuan et al., TVGC09 Both techniques are popular! Yet, we know very little about their comparative advantages. Claessen & van Wijk, TVGC11

  7. Basics of Evaluation Research question • What’s the comparative advantages between PCP & SCP for certain tasks? Task Independent variables Dependent variables

  8. Basics of Evaluation Research question • What’s the comparative advantages between PCP & SCP for certain tasks? Task Independent variables Dependent variables

  9. Basic Analytical Tasks Amar et al.: Low-level components of analytic activity in information visualization. InfoVis05, 111–117. serves as a subtask for many other tasks (Holten & van Wijk, EuroVis10) PCP is inferior than SCP (Li et al.,InfoVis10)

  10. Value Retrieval Task Multi-Variate Data Tuple (X1, X2, X3, …. , Xn) a ? Definition: • Given the numerical value of one attribute of a data tuple, find the numerical value of another attribute of the same data tuple.

  11. Basics of Evaluation Research question • What’s the comparative advantages between PCP & SCP for certain tasks? Task Independent variables Dependent variables

  12. Independent Variables Technique Parallel Coordinate Plot (PCP) SCP-rotated (Qu et al., TVCG07) Scatter Plot (SCP) X3 X3 X1 X2 X2 X1 X3 X2 X2 X4 X2 X2 X2 X3 X1 X4 X3 X4 SCP-common (SPLOM) SCP-staircase(Viau et al., TVCG10)

  13. Independent Variable – 4 Technique PCP SCP-rotated (i.e., Qu et al., TVCG07) SCP-common (i.e., SPLOM) SCP-staircase (i.e., Viau et al., TVCG10)

  14. Additional Independent Variables Number of Dimensions X3 X1 X2 X2 X3 X3 X4 X1 X2 X1 Data Density X2 X2 X2 X3 X4 X3 X4 X4 X5 X3

  15. Independent Variables Technique Dimension Density

  16. Dependent Variables Completion time Error distance

  17. Experiment Demo

  18. Experiment 1 Design 12 participants × 4 visualization techniques(PCP, SCP-common, SCP-rotate, SCP-standard) × 3 levels of data dimension(2D, 4D, 6D) × 3 levels of data density (10 tuples, 20 tuples, 30 tuples) × 3 repetitions of trials = 1296 trials in total.

  19. Overall Results Completion Time Error Distance Poorer Poor Error Distance Poor Seconds Good Best Good PCP PCP SCP-rotate SCP-rotate SCP-common SCP-common SCP-staircase SCP-staircase

  20. 1st Take-away Lesson PCP SCP-rotated (i.e., Qu et al., TVCG07) SCP-common (i.e., SPLOM) SCP-staircase (i.e., Viau et al., TVCG10)

  21. PCP vs. SCP-common

  22. PCP vs. SCP-common Performance Difference Density

  23. PCP vs. SCP-common Performance Switch Order Density

  24. Important Observation There seems to be a Density & Number of Dimension Trade-off between PCP & SCP-common!

  25. Experiment 2 × 18 participants × 2 techniques (PCP, SCP-common) × 3 dimensions (4D, 6D, 8D) [2D, 4D, 6D in Exp. 1] × 3 densities (20 tuples, 30 tuples, 40 tuples) [10, 20, 30 in Exp. 1] × 5 trials for each combination = 1620 trials in total.

  26. Results – Completion Time Overall result for Exp. 2 Result in Exp. 1 SCP-common (12.02s) PCP (8.99s) SCP-common (15.41s) PCP (18.23s) faster faster Trade-off between number of dimensions & data density Density Dimension

  27. Results – Error Distance Trade-off between number of dimensions & data density Density Dimension

  28. Take-away Lessons Dimension Density The value retrieval performance of PCP increases depending on dimensionality. The performance of SCP-common seems independent of dimensionality. Increasing density affects the performance of PCP more than it affects SCP-common.

  29. Let’s Recap the Take Away-Messages and Ask Why 1) Both SCP-rotate and SCP-staircase are inferior for value retrieval task

  30. Let’s Recap the Take Away Messages 2) Performance trade-off between PCP & SCP-common for both dimensionalities anddata density. • PCP increases depending on dimensionality. • SCP-common performance seems to be independent.

  31. Let’s Recap the Take Away Messages 10 tuples 2) Performance trade-off between PCP & SCP-common for both dimensionalities and data density. • PCP increases depending on dimensionality. • SCP-common performance seems to be independent. • Increasing density affects the performance of PCP more than it affects SCP-common. 40 tuples

  32. Conclusion and Future Work Our study helps to understand the comparative advantages between PCP & SCP However, this is only a starting point,

  33. The Grand Vision InfoVis evaluation package Results/ Recommendations Ideally, this problem can be solved by …

  34. Acknowledgment This research is supported by: The National University of Singapore Academic Research Fund R-252-000-375-133 and by: The Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office.

  35. Q & A Elastic Hierarchy (InfoVis ‘05) Tracing Tuples Across Dimensions (EuroVis ‘12)

  36. End

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