Marti Hearst SIMS 247 SIMS 247 Lecture 5 Brushing and Linking February 3, 1998.

Marti HearstSIMS 247

SIMS 247 Lecture 5SIMS 247 Lecture 5Brushing and LinkingBrushing and Linking

February 3, 1998February 3, 1998


TodayToday

• Interactive techniquesInteractive techniques– Highlighting– Brushing and Linking

• Example systemsExample systems– Graham Will’s system– Tweedie’s Influence Explorer– Ahlberg & Sheiderman’s IVEE (Spotfire)– Roth et al.’s VISAGE


Review: Why Use Visualizations?Review: Why Use Visualizations?

• Persuade Persuade (Lott rebuttal to State of Union speech)(Lott rebuttal to State of Union speech)

• ExplainExplain (Organizational chart, life cycle of (Organizational chart, life cycle of worm)worm)

• ExploreExplore (Inselberg chip detective story)(Inselberg chip detective story)

• Analyze Analyze (Challenger accident)(Challenger accident)

• (Entertain, Amuse)(Entertain, Amuse)


Some Roles of Visualization in Some Roles of Visualization in Exploring Large Data Sets Exploring Large Data Sets (Wills 95)(Wills 95)

• Data validationData validation• Outlier detectionOutlier detection• Suggestion and evaluation of Suggestion and evaluation of

modelsmodels• Discovery of relationships among Discovery of relationships among

subsets of datasubsets of data


Interactive TechniquesInteractive Techniques

• Ask what-if questions Ask what-if questions spontaneously while working spontaneously while working through a problemthrough a problem

• Control the exploration of subsets Control the exploration of subsets of data from different viewpointsof data from different viewpoints


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


Highlighting: selection within one graph Highlighting: selection within one graph (from Schall 95)(from Schall 95)


BrushingBrushing

• An interactive techniqueAn 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 At least two things must be linkedlinked together to allow for brushingtogether to allow for brushing


Link similar types of graphs:Link similar types of graphs:Brushing a Scatterplot MatrixBrushing a Scatterplot Matrix

(Figure from Tweedie et al. 96; (Figure from Tweedie et al. 96; See also Cleveland & McGill 84, 88)See also Cleveland & McGill 84, 88)


Link different types of graphs:Link different types of graphs:Scatterplots and histograms and bars Scatterplots and histograms and bars

(from Wills 95)(from Wills 95)


Baseball data:Baseball data:Scatterplots and histograms and barsScatterplots and histograms and bars

(from Wills 95)(from Wills 95)

select highsalaries

avg careerHRs vs avg career hits(batting ability)

avg assists vsavg putouts (fielding ability)

how longin majors

distributionof positionsplayed


What was learned from interaction What was learned from interaction with this baseball data?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?


Linking types of assist behavior to Linking types of assist behavior to position played position played (from Wills 95)(from Wills 95)


Animating brushing on fielding informationAnimating brushing on fielding information(Look at Lucent’s EDV(Look at Lucent’s EDV

http://www.bell-labs.com/user/gwills/EDVguide/bb.htmlhttp://www.bell-labs.com/user/gwills/EDVguide/bb.html))


Influence ExplorerInfluence Explorer (Tweedie et al. 96)(Tweedie et al. 96)

• Manufacturing light bulbsManufacturing light bulbs• A set of equations relateA set of equations relate

– parameters (values chosen by designer) to– performance

• Goal: find parameter values for a Goal: find parameter values for a desired kind of performancedesired kind of performance– Example: How to build a very bright bulb

that lasts for 6 months?


Traditional Design ProcessTraditional Design Process

• Can go from parameters -> performanceCan go from parameters -> performance• Can’t do the reverse!Can’t do the reverse!• Standard solution:Standard solution:

– guess some parameters– compute results– adjust parameters– iterate until get close to desired performance

• Time-consuming and tedious!Time-consuming and tedious!


Using a ModelUsing a Model

• Choose a region in parameter space Choose a region in parameter space that covers a large number of pointsthat covers a large number of points

• Compute the resulting design space for Compute the resulting design space for all these pointsall these points


Another difficultyAnother difficulty

• Cannot design for only Cannot design for only one pointone point in in the performance spacethe performance space– Manufacturing process is variable– Must define a tolerance region

region of acceptibility:the desired performance space

yield isthe intersectionis where theusable bulbswill end up


Influence ExplorerInfluence Explorer

• Goals:Goals:– Large yields– Low cost (from wider tolerances)

• Approach:Approach:– Introduce complexity in stages– Give designer a qualitative understanding– Interactivity allows designer to quickly

explore tradeoffs among settings


An Innovation! Show how many An Innovation! Show how many items fail by one, two, or three items fail by one, two, or three

performance criteria performance criteria (Tweedie et al. 96)(Tweedie et al. 96)


Also restrict the range of parameter Also restrict the range of parameter settings. How many constraints settings. How many constraints

away from success? away from success? (Tweedie et al. 96)(Tweedie et al. 96)

Coding seems complex initially, but suits the designers’ needs and is easily learned.


Previous figure with re-codingPrevious figure with re-coding


References for this LectureReferences for this Lecture

• Wills, Graham J. Visual Exploration of Large Structured Datasets, Wills, Graham J. Visual Exploration of Large Structured Datasets, New New Techniques and Trends in Statistics, Techniques and Trends in Statistics, 237-246. IOS Press, 1995. 237-246. IOS Press, 1995. http://www.bell-labs.com/user/gwills/ntts95/paper.htmlhttp://www.bell-labs.com/user/gwills/ntts95/paper.html

• Lucent’s EDV guide. http://www.bell-labs.com/user/gwills/EDVguide/bb.htmlLucent’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. Cleveland, W.S. and McGill, R. The Many Faces Of A Scatterplot. Journal of Journal of

the American Statistical Association, 79,the American Statistical Association, 79, pp. 807-822, 1984. pp. 807-822, 1984. • Cleveland, W.S. and McGill, R., eds. Dynamic Graphics For Statistics. Cleveland, W.S. and McGill, R., eds. Dynamic Graphics For Statistics.

Wadsworth & Brooks, 1988.Wadsworth & Brooks, 1988.• Tweedie, Lisa, Spence, Robert, Dawkes, Huw, and Su, Hua. Externalising Tweedie, Lisa, Spence, Robert, Dawkes, Huw, and Su, Hua. Externalising

Abstract Mathematical Models. Proceedings of ACM SIGCHI, April 1996. Abstract Mathematical Models. Proceedings of ACM SIGCHI, April 1996. http://www.ee.ic.ac.uk/research/information/www/LisaDir/CHI96/lt1txt.htmlhttp://www.ee.ic.ac.uk/research/information/www/LisaDir/CHI96/lt1txt.html

• Roth, Steven F., Chuah, Mei C., Kerpedjiev, Stephan, Kolojejchick, John, and Roth, Steven F., Chuah, Mei C., Kerpedjiev, Stephan, Kolojejchick, John, and Lucas, Peter. Towards an Information Visualization Workspace: Combining Lucas, Peter. Towards an Information Visualization Workspace: Combining Multiple Means of Expression. Multiple Means of Expression. Human-Computer Interaction JournalHuman-Computer Interaction Journal, , 1997, in 1997, in press.press.

• Schall, Matthew. SPSS DIAMOND: a visual exploratory data analysis tool. Schall, Matthew. SPSS DIAMOND: a visual exploratory data analysis tool. Perspective, 18 (2), Perspective, 18 (2), 1995. http://www.spss.com/cool/papers/diamondw.html1995. http://www.spss.com/cool/papers/diamondw.html

Marti Hearst SIMS 247 SIMS 247 Lecture 5 Brushing and Linking February 3, 1998.

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