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 1 (Faculty member, École de technologie supérieure)

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

3. 3 of Vienna Singapore 9697 km

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

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

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

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

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

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

10. Value Retrieval Task Definition: Given the numerical value of one attribute of a data tuple, find the numerical value of another attribute of the same data tuple. 10 Multi-Variate Data Tuple (X 1, X 2, X 3, …., X n ) a?

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

12. Independent Variables 12 Technique Parallel Coordinate Plot (PCP) Scatter Plot (SCP) X2X2 X1X1 X3X3 X2X2 X4X4 X3X3 X1X1 X2X2 X3X3 X2X2 X4X4 X2X2 X2X2 X1X1 X2X2 X3X3 X4X4 X3X3 SCP-rotated (Qu et al., TVCG07) SCP-common (SPLOM ) SCP-staircase (Viau et al., TVCG10)

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

14. Additional Independent Variables 14 X2X2 X1X1 X3X3 X2X2 X4X4 X3X3 Number of Dimensions X2X2 X1X1 X3X3 X2X2 X4X4 X3X3 X5X5 X4X4 Data Density X2X2 X1X1 X3X3 X2X2 X4X4 X3X3

15. Independent Variables Technique Dimension Density 15

16. Dependent Variables Completion time Error distance 16

17. Experiment Demo 17

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. 18

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

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

21. PCP vs. SCP-common 21

22. PCP vs. SCP-common 22 Density Performance Difference

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

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

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. 25

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

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

28. Take-away Lessons 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. 28 Dimension Density

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

30. Let’s Recap the Take Away Messages 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. 30

31. Let’s Recap the Take Away Messages 31 10 tuples 40 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.

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

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

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 35 Elastic Hierarchy (InfoVis ‘05) Tracing Tuples Across Dimensions (EuroVis ‘12)

36. End 36