Authors: Xiaole Kuang, Haimo Zhang, Shengdong Zhao, Michael J. McGuffin

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!

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 (X1, X2, X3, …. , Xn)
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)
X2
X1
X3
X2
X4
X3
X1
X2
X3
X2
X4
X2
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
X2
X1
X3
X2
X4
X3
Number of Dimensions
X2
X1
X3
X2
X4
X3
X5
X4
Data Density
X2
X1
X3
X2
X4
X3

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
ErrorDistance
SCP-rotate
SCP-common SCP-staircase
PCP
Poor
Good
Poorer

20. 1st 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
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

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