Presentation for Info863 at CCI, Drexel University. Paper is about Christopher G. Healey, and et al.'s paper published in 2008.

1. Visual Perception and Mixed-
Initiative Interaction for Assisted
Visualization Design
by Christopher G. Healey, etc.
Info863
Kai Li
10/27/2015

2. What the paper is about?
• The paper is about the design of a
visualization system called “ViA” using the
techniques of
– Perceptual guidelines from human vision, and
– AI-based mixed-initiative search strategy
• and an evaluation of the system on these two
aspects.

3. What’s new in ViA?
• Shortcomings of existing visualization systems:
– They cannot suggest users how to best represent
their data based on their needs, and,
– They have limited abilities to integrate perceptual
rules in the creation of visualizations.
• Users who are not visualization researchers
often repeat the same design principles,
which causes a number of inefficiencies,
especially for multidimensional datasets.

4. ViA’s architecture
• The goal of the ViA project is to help users getting
access to a collection of visualization designs using the
same data, and construct visualizations for their own
data, that are effective, multidimensional, transparent,
application independent, and extensible.
– ViA seems to create only 2D and static visualizations.
• Components of ViA:
– User input: attribute importance, spatial frequency, value
type, task.
– Evaluation engine
– Hints: feature swap, importance weight modify, discretize,
task removal.
– Search algorithm

5. ViA’s architecture

6. Perceptual foundations of information
visualization
• Visualization is the mapping between data properties
to visual properties.
– M(V, Φ)
• Previous studies on how human visual system actually
“sees” fundamental properties of color and texture in
an image formed the foundation of the visualization in
this study.
– Color:
• Luminance, hue, saturation…
– Texture:
• Size, spatial packing density, orientation, regularity…
– Interactions between visual properties

8. • Visualization of 2D flow in a simulated supernova
collapse
– Stroke orientation: flow direction
– Color: magnitude
– Size: pressure

9. Mixed-Initiative Interaction
• Mixed-initiative interaction “[combines]
automated services and user control to form
mixed-initiative interaction.”
– Participants are enabled to contribute their
unique strengths towards solving a common
problem.
– Controls are shifted between parties who are the
most qualified to solved the problems in each
step.

10. How mixed-initiative interaction is
applied to ViA
• Consideration: how to manage the uncertainty
about a user’s goals during problem solving.
• A common solution is “to use Bayesian agents to
model goals and construct utility measures based
on probabilistic relationships”

11. Evaluation of ViA
• Three separate components are considered
for evaluating ViA’s performance:
– ViA’s ability to locate the best visualization
mappings, compared with an exhaustive search
and simulated annealing and reactive tabu search
algorithms
– Mixed-initiative interaction’s influences on ViA’s
visualization recommendation
– How ViA system is applied in another project
focusing on E-commerce auction environments

12. Evaluation of search performance
• Three metrics were calculated to evaluate the
search performance:
– Optimality: the evaluation weight of the best mapping
found by an algorithm
– Efficiency: the number of visualization evaluated
before an algorithm finds the first optimal mapping
– Completeness: the total number of visualizations
found by an algorithm relative to the total number of
mappings with the maximum evaluation weight.

13. Evaluation of search performance
(cont.)
• ViA (Hint) produced the
highest efficiency when it
found the first optimal result,
but also got the least number
of optimal mappings among
the three algorithms.
• The authors argue that hint-
based search, because its
searching procedure is based
on hints rather than local
regions (RTS), has the
potential to generate diverse
results.

14. Evaluation of Mixed-initiative interaction’s
influences on recommendation
• Experiments were conducted in three modes:
– Without mixed-initiative interaction (ViA-N). System was run by
fixed settings.
– With mixed-initiative interaction. Decisions are controlled solely
by expected utility (ViA-MI)
– With mixed-initiative interaction. Decisions made by users (ViA-
UI)

15. Limitations of ViA
• Visualizations created ViA are based on
geometrics glyphs, which might not fit certain
types of datasets and analysis.
• ViA is application-independent, thus may not
support some domains very well.
• No controlled experiments have been conducted
to prove ViA’s advantages in real-world contexts.
• Users might not agree with ViA about which
visualization is the “best” choice.

16. Compare and contrast

17. Compare and contrast (cont.)

18. Compare and contrast (cont.)

19. Questions
• Is there better (or best) visualizations?
– What criteria are we using to evaluate?
• This paper seems to evaluate visualizations based on
perceptual theories (how different data properties
should be mapped to visual cues, and how these visual
cues interact with each other), which is sort of an etic
perspective and only necessary conditions.
– What other criteria other than those in this paper
can we use to evaluate visualizations?
• http://hint.fm/wind/

20. Reference
• Healey, C., Kocherlakota, S., Rao, V., Mehta, R.,
& St Amant, R. (2008). Visual perception and
mixed-initiative interaction for assisted
visualization design. IEEE Transactions on
Visualization and Computer Graphics, 14(2),
396–411.
http://doi.org/10.1109/TVCG.2007.70436

21. Thank you!