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SIMS 247 Lecture 5 Brushing and Linking

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

  2. 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

  3. 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

  4. 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

  5. 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

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

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

  8. 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

  9. 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

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

  11. 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

  12. 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

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

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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

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

  21. 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

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

  23. 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

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