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

2. Marti Hearst SIMS 247 Today 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

3. Marti Hearst SIMS 247 Review: Why Use Visualizations? PersuadePersuade (Lott rebuttal to State of Union speech) ExplainExplain (Organizational chart, life cycle of worm) ExploreExplore (Inselberg chip detective story) AnalyzeAnalyze (Challenger accident) (Entertain,(Entertain, Amuse)

4. Marti Hearst SIMS 247 Some Roles of Visualization in Exploring Large Data Sets (Wills 95) Data validationData validation Outlier detectionOutlier detection Suggestion and evaluation of modelsSuggestion and evaluation of models Discovery of relationships among subsets of dataDiscovery of relationships among subsets of data

5. Marti Hearst SIMS 247 Interactive Techniques Ask what-if questions spontaneously while working through a problemAsk what-if questions spontaneously while working through a problem Control the exploration of subsets of data from different viewpointsControl the exploration of subsets of data from different viewpoints

6. Marti Hearst SIMS 247 Highlighting (Focusing) Focus user attention on a subset of the data within one graph (from Wills 95)

7. Marti Hearst SIMS 247 Highlighting: selection within one graph (from Schall 95)

8. Marti Hearst SIMS 247 Brushing 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 linked together to allow for brushingAt least two things must be linked together to allow for brushing

9. Marti Hearst SIMS 247 Link similar types of graphs: Brushing a Scatterplot Matrix (Figure from Tweedie et al. 96; See also Cleveland & McGill 84, 88)

10. Marti Hearst SIMS 247 Link different types of graphs: Scatterplots and histograms and bars (from Wills 95)

11. Marti Hearst SIMS 247 Baseball data: Scatterplots and histograms and bars (from Wills 95) select high salaries avg career HRs vs avg career hits (batting ability) avg assists vs avg putouts (fielding ability) how long in majors distribution of positions played

12. Marti Hearst SIMS 247 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?

13. Marti Hearst SIMS 247 Linking types of assist behavior to position played (from Wills 95)

14. Marti Hearst SIMS 247 Animating brushing on fielding information (Look at Lucent’s EDV http://www.bell-labs.com/user/gwills/EDVguide/bb.html )

15. Marti Hearst SIMS 247 Influence Explorer (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 desired kind of performanceGoal: find parameter values for a desired kind of performance –Example: How to build a very bright bulb that lasts for 6 months?

16. Marti Hearst SIMS 247 Traditional 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!

17. Marti Hearst SIMS 247 Using a Model Choose a region in parameter space that covers a large number of pointsChoose a region in parameter space that covers a large number of points Compute the resulting design space for all these pointsCompute the resulting design space for all these points

18. Marti Hearst SIMS 247 Another difficulty Cannot design for only one point in the performance spaceCannot 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

19. Marti Hearst SIMS 247 Influence 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

20. Marti Hearst SIMS 247 An Innovation! Show how many items fail by one, two, or three performance criteria (Tweedie et al. 96)

21. Marti Hearst SIMS 247 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.

22. Marti Hearst SIMS 247 Previous figure with re-coding

23. Marti Hearst SIMS 247 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.htmlWills, 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.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. Journal of the American Statistical Association, 79, pp. 807-822, 1984.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.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.htmlTweedie, 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.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.htmlSchall, Matthew. SPSS DIAMOND: a visual exploratory data analysis tool. Perspective, 18 (2), 1995. http://www.spss.com/cool/papers/diamondw.html