1. Brushing, Linking & Interactive Querying Information Visualization February 15, 2002 Sarah Waterson

2. Interaction “Interaction involves the transformations that map the data to visual form.” More than just the controls? Integrate controls into the visualization. Allow for direct manipulation of the graphical representation of the data.

3. Exploratory Data Analysis Beyond the small multiples - the next generation of Exploratory Data Analysis! Detective work – spot trends, patterns, errors, features in the data. “Unless exploratory data analysis uncovers indications, usually quantitative ones, there is likely to be nothing for confirmatory data analysis to consider.”

4. Time Response times of computer must be tuned to human response times 1.Psychological Moment (0.1 sec.) Fusion into single precept: motion, animation, cause & effect 2.Unprepared Response (1 sec.) dialogue, driving, updating user 3.Unit Task (~10 sec.) elementary interaction cycles, pace of routine cognitive skills

5. Overview of Papers “High Interaction Graphics” Stephen G. Eick & Graham J. Wills, AT&T Bell Labs 1994 “Dynamic Queries for Visual Information Seeking” Ben Shneiderman, U. of Maryland 1994 “Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays” Christopher Ahlberg & Ben Shneiderman, U. of Maryland 1994 “Data Visualization Sliders” Stephen G. Eick, AT&T Bell Labs 1994 “Interactive Data Analysis: The Control Project” Joseph Hellerstein & Co., U.C. Berkeley & IBM Almaden 1999 “Enhanced Dynamic Queries via Movable Filters” Ken Fishkin & Maureen C. Stone, Xerox PARC 1995

6. High Interaction Graphics Clarity Information only on demand, cleaner & more focused displays, allow a range of options Robustness Avoid drawing inferences from only one view Power Combine views, leverage exploration Possibility 3+ dimensional data, animation

7. Principles 1.Simple, easy to interpret views 2.Information hiding, details on demand 3.Direct Manipulation

8. Linking & Brushing Linking Visually indicating which parts of one data display correspond to that of another Brushing Allowing the user to move a region (brush) around the data display to highlight groups of data points. Generally used on scatter plots. Usability issues: selection, de-selection, setting values, appropriate widgets

9. Examples Districts of the city of Dublin showing areas with high levels of average income Linking altitude to grass and grain types in Scottish Districts

10. Another Example Point Visualization Tool (PVT) of data related by postal codes

11. Application Domains Spatial Data Visualization “In general, there are more assumptions made about spatial data than about non- spatial data and thus more diagnostic plots are required.” Software Visualization Very difficult problem with many dimensions and possible visualizations: the code, data structures, communication, execution threads, debugging, memory management, etc. SeeSoft

12. Comments Great introduction of purpose, general techniques. Some mention of usability, though more would be appreciated. Examples were somewhat simple, despite mentioning complex application domains. Easy to read. Seems like the beginnings of a book or survey paper.

13. Dynamic Queries Selecting value ranges of variables via controls with real time feedback in the display Selection by pointing, not typing Immediate and continuous feedback Support browsing Details on demand Principles: Visual presentation of query’s components Visual presentation of results Rapid, incremental, and reversible control

14. Examples Periodic Table of the Elements Adjust properties with sliders on the bottom to highlight matching elements.

15. More Examples DynaMap Cervical cancer rates from 1950- 1970 - modify year, state, demographics Unix Directory Exploration

16. Even More Examples

17. Yet More Examples Information Visualization and Exploration Environment (IVEE) Job to Skills matching Devise

18. Coupling Starfield Displays Tight coupling Query components are interrelated in ways that preserve display invariants, reveal state of system Output of queries can be easily used as input to produce other queries. Eliminate distinction between commands/queries/input and results/tables/output Starfields For data without natural mapping Glorified scatter plots?

19. Home Finder: Map

20. Home Finder: Text

21. Film Finder

23. Pros & Cons Quick, easy, safe, & playful Good for novices & experts Excellent for exploration of very large data sets Database management systems can’t handle the queries Slow hardware Application specific programming Simple queries only So many controls…

24. Research Directions Widgets for multiple ranges Boolean combinations for sliders Zooming Selecting controls from large sets of attributes Grand tours of the data New interaction devices

25. Comments Good paper for overview, purpose and research directions for dynamic queries. Particularly for research directions. Compelling examples for need. Usability study showed dynamic queries faster than Symantec's Q&A, though other measures might be more important than speed. Well written. Big impact & contribution to the field.

26. Data Visualization Sliders Use the sliders themselves as data displays “Painting” metaphor for specifying disconnected intervals

27. The Control Project Continuous Output and Navigation Technology with Refinement Online “Of all men’s miseries, the bitterest is this: to know so much and have control over nothing.” Herodotus Full scale data analysis will always be slow. Goal: Build a system that iteratively refines answers to queries and give users online control of processing. Aggregation, Enumeration, Visualization, Mining

28. The Crystal Ball Anytime Algorithms produce a meaningful approximate result at any time during their execution Trade quality and accuracy for interactive response times Continuously fetch new data at random – users prefer a to see a representative sample of the data at any time Preferential re-ordering Ripple joins

29. Online Aggregation

30. Online Enumeration – UI Database analysts vs. Domain experts Eyeballing in Databases and lists Using fuzzy techniques, such as the scrollbar

31. Clouds Render records as they are fetched but also generate overlay of shaded regions estimating missing data. Cloud color chosen to minimize expected error. Online Data Visualization

32. Comments Great work. Really cool. Big impact. Very necessary technology, intelligent solution, and very compelling. More analysis of the visualization would be nice and perhaps more on usability (Katie Everitt and Ka-Ping Yee) Overall, quite impressive.

33. Movable Filters Movable Magic Lens TM filters over starfield displays for multiple simultaneous visual transformations and queries Enhanced brushing with sliders?

34. Queries & Filters Boolean Composition Semantic Filters Real-valued Queries Missing Values

35. Comments Interesting idea, but I would like to see it in action The UI looks a bit horrid and no usability studies Only seems appropriate for scatter plots, and selection is limited by shape Good that it can do some more complex queries, but are they understandable? Where else could one use these lenses?

36. Thoughts More than Mice Interaction techniques beyond point and click Understanding the Data Understanding the data and model – How to create the interface appropriate for investigation.