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Interaction Week 10 CPSC 533C, Spring 2004

Interaction Week 10 CPSC 533C, Spring 2004. 11 Feb 2004 Tamara Munzner. Ware: Interaction Cognitive Co-Processor SDM Dynamic Queries Toolglass/Magic Lenses more interaction Influence Explorer LifeLines Hierarchical Clustering Explorer Exploratory Data Views ThemeScapes video (if time).

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Interaction Week 10 CPSC 533C, Spring 2004

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  1. InteractionWeek 10 CPSC 533C, Spring 2004 11 Feb 2004 Tamara Munzner

  2. Ware: Interaction • Cognitive Co-Processor • SDM • Dynamic Queries • Toolglass/Magic Lenses • more interaction • Influence Explorer • LifeLines • Hierarchical Clustering Explorer • Exploratory Data Views • ThemeScapes video (if time)

  3. Ware Interaction • control loops • Fitts’ Law • time to select depends on distance, target size • learning • power law of practice • vigilance • difficult, erodes with fatigue • navigation • next time

  4. Ware Interaction • rapid interaction • dynamic queries • brushing (linked highlighting) • problem solving loops • external representations • external memory vs internal (working, LT, ST) • memory extensions enable otherwise impossible tasks • visual spatial reasoning

  5. Cognitive Co-Processor • animated transitions • object constancy • fixed frame rate required • architectural solution • split work into small chunks • animation vs. idle states • governor controls frame rate • [video: 3D rooms]

  6. SDM • sophisticated selection, highlighting, • object manipulation • [video]

  7. Dynamic Queries: HomeFinder • filter with immediate visual feedback • “starfield”: scatterplot • [video]

  8. DQ 2: FilmFinder

  9. DQ 2: FilmFinder

  10. Toolglass/Magic Lens symmetry glass • see-through • two-handed curvature lens Figure 12. Gaussian curvature pseudo-color lens with overlaid tool to read the numeric value of the curvature. (Original images courtesy of Steve Mann)

  11. More Linked Views key infovis interaction principle so far: Ware, trellis, xgobi, cluster calendar, parallel coords brushing, ... new examples: Attribute Explorer LifeLines Hierarchical Clustering Explorer EDV

  12. Influence/Attribute Explorer • Visualization for Functional Design, Bob Spense, Lisa Tweedie, Huw Dawkes, Hua Su, InfoVis 95 [video]

  13. LifeLines • LifeLines: Visualizing Personal Histories. B. Milash, C. Plaisant, A. Rose (University of Maryland). CHI 96 Video Program • [video]

  14. EDV Exploratory Data Visualizer Graham J. Wills. Visual Exploration of Large Structured Datasets. In New Techniques and Trends in Statistics, 237-246. IOS Press, 1995.

  15. Highlighting (Focusing) Focus user attention on a subset of the data within one graph (from Wills 95) [www.sims.berkeley.edu/courses/is247/s02/lectures/Lecture3.ppt]

  16. Link different types of graphs:Scatterplots and histograms and bars(from Wills 95) [www.sims.berkeley.edu/courses/is247/s02/lectures/Lecture3.ppt]

  17. 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 [www.sims.berkeley.edu/courses/is247/s02/lectures/Lecture3.ppt]

  18. Linking types of assist behavior to position played (from Wills 95) [www.sims.berkeley.edu/courses/is247/s02/lectures/Lecture3.ppt]

  19. HCE Hierarchical Clustering Explorer Jinwook Seo, Ben Shneiderman, "Interactively Exploring Hierarchical Clustering Results," IEEE Computer, Volume 35, Number 7, pp. 80-86, July 2002.

  20. HCE • Linked views • Dendrogram navigation • Comparing multiple dendrograms [demo]

  21. False negatives vs. positives [graphics.stanford.edu/courses/cs448b-02-spring/lectures/interaction/walk036.html

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