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SIMS 247 Lecture 5 Brushing and Linking. February 3, 1998. Today. Interactive techniques Highlighting Brushing and Linking Example systems Graham Will’s system Tweedie’s Influence Explorer Ahlberg & Sheiderman’s IVEE (Spotfire) Roth et al.’s VISAGE. Review: Why Use Visualizations?.

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SIMS 247 Lecture 5 Brushing and Linking


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sims 247 lecture 5 brushing and linking

SIMS 247 Lecture 5Brushing and Linking

February 3, 1998

Marti Hearst

SIMS 247

today
Today
  • Interactive techniques
    • Highlighting
    • Brushing and Linking
  • Example systems
    • Graham Will’s system
    • Tweedie’s Influence Explorer
    • Ahlberg & Sheiderman’s IVEE (Spotfire)
    • Roth et al.’s VISAGE

Marti Hearst

SIMS 247

review why use visualizations
Review: Why Use Visualizations?
  • Persuade (Lott rebuttal to State of Union speech)
  • Explain (Organizational chart, life cycle of worm)
  • Explore (Inselberg chip detective story)
  • Analyze (Challenger accident)
  • (Entertain, Amuse)

Marti Hearst

SIMS 247

some roles of visualization in exploring large data sets wills 95
Some Roles of Visualization in Exploring Large Data Sets (Wills 95)
  • Data validation
  • Outlier detection
  • Suggestion and evaluation of models
  • Discovery of relationships among subsets of data

Marti Hearst

SIMS 247

interactive techniques
Interactive Techniques
  • Ask what-if questions spontaneously while working through a problem
  • Control the exploration of subsets of data from different viewpoints

Marti Hearst

SIMS 247

highlighting focusing
Highlighting (Focusing)

Focus user attention on a subset of the data within one graph (from Wills 95)

Marti Hearst

SIMS 247

brushing
Brushing
  • An interactive technique
    • select a subset of points
    • see the role played by this subset of points in one or more other views
  • At least two things must be linked together to allow for brushing

Marti Hearst

SIMS 247

slide9
Link similar types of graphs:Brushing a Scatterplot Matrix(Figure from Tweedie et al. 96; See also Cleveland & McGill 84, 88)

Marti Hearst

SIMS 247

link different types of graphs scatterplots and histograms and bars from wills 95
Link different types of graphs:Scatterplots and histograms and bars (from Wills 95)

Marti Hearst

SIMS 247

baseball data scatterplots and histograms and bars from wills 95
Baseball data:Scatterplots and histograms and bars(from Wills 95)

how long

in majors

select high

salaries

avg career

HRs vs avg

career hits

(batting ability)

avg assists vs

avg putouts

(fielding ability)

distribution

of positions

played

Marti Hearst

SIMS 247

what was learned from interaction with this baseball data
What was learned from interaction with this baseball data?
  • Seems impossible to earn a high salary in the first three years
  • High salaried players have a bimodal distribution (peaking around 7 & 13 yrs)
  • Hits/Year a better indicator of salary than HR/Year
  • High paid outlier with low HR and medium hits/year. Reason: person is player-coach
  • There seem to be two differentiated groups in the put-outs/assists category (but not correlated with salary) Why?

Marti Hearst

SIMS 247

slide14
Animating brushing on fielding information(Look at Lucent’s EDVhttp://www.bell-labs.com/user/gwills/EDVguide/bb.html)

Marti Hearst

SIMS 247

influence explorer tweedie et al 96
Influence Explorer(Tweedie et al. 96)
  • Manufacturing light bulbs
  • A set of equations relate
    • parameters (values chosen by designer) to
    • performance
  • Goal: find parameter values for a desired kind of performance
    • Example: How to build a verybright bulb that lasts for 6 months?

Marti Hearst

SIMS 247

traditional design process
Traditional Design Process
  • Can go from parameters -> performance
  • Can’t do the reverse!
  • Standard solution:
    • guess some parameters
    • compute results
    • adjust parameters
    • iterate until get close to desired performance
  • Time-consuming and tedious!

Marti Hearst

SIMS 247

using a model
Using a Model
  • Choose a region in parameter space that covers a large number of points
  • Compute the resulting design space for all these points

Marti Hearst

SIMS 247

another difficulty
Another difficulty
  • Cannot design for only one point in the performance space
    • Manufacturing process is variable
    • Must define a tolerance region

region of

acceptibility:

the desired

performance

space

yield is

the intersection

is where the

usable bulbs

will end up

Marti Hearst

SIMS 247

influence explorer
Influence Explorer
  • Goals:
    • Large yields
    • Low cost (from wider tolerances)
  • Approach:
    • Introduce complexity in stages
    • Give designer a qualitative understanding
    • Interactivity allows designer to quickly explore tradeoffs among settings

Marti Hearst

SIMS 247

an innovation show how many items fail by one two or three performance criteria tweedie et al 96
An Innovation! Show how many items fail by one, two, or three performance criteria (Tweedie et al. 96)

Marti Hearst

SIMS 247

slide21
Also restrict the range of parameter settings. How many constraints away from success? (Tweedie et al. 96)

Coding seems complex initially, but suits

the designers’ needs and is easily learned.

Marti Hearst

SIMS 247

previous figure with re coding
Previous figure with re-coding

Marti Hearst

SIMS 247

references for this lecture
References for this Lecture
  • Wills, Graham J. Visual Exploration of Large Structured Datasets, New Techniques and Trends in Statistics, 237-246. IOS Press, 1995. http://www.bell-labs.com/user/gwills/ntts95/paper.html
  • Lucent’s EDV guide. http://www.bell-labs.com/user/gwills/EDVguide/bb.html
  • Cleveland, W.S. and McGill, R. The Many Faces Of A Scatterplot. Journal of the American Statistical Association, 79, pp. 807-822, 1984.
  • Cleveland, W.S. and McGill, R., eds. Dynamic Graphics For Statistics. Wadsworth & Brooks, 1988.
  • Tweedie, Lisa, Spence, Robert, Dawkes, Huw, and Su, Hua. Externalising Abstract Mathematical Models. Proceedings of ACM SIGCHI, April 1996. http://www.ee.ic.ac.uk/research/information/www/LisaDir/CHI96/lt1txt.html
  • Roth, Steven F., Chuah, Mei C., Kerpedjiev, Stephan, Kolojejchick, John, and Lucas, Peter. Towards an Information Visualization Workspace: Combining Multiple Means of Expression. Human-Computer Interaction Journal, 1997, in press.
  • Schall, Matthew. SPSS DIAMOND: a visual exploratory data analysis tool. Perspective, 18 (2), 1995. http://www.spss.com/cool/papers/diamondw.html

Marti Hearst

SIMS 247