Interaction II Maneesh Agrawala CS 448B: Visualization Fall 2018 - - PDF document

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Interaction II Maneesh Agrawala CS 448B: Visualization Fall 2018 - - PDF document

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Interaction II Maneesh Agrawala CS 448B: Visualization Fall 2018 1 Last Time: Interaction Gulfs of execution & evaluation Gulfs Evaluation Conceptual model Real world Execution [Norman 1986] 2 [Graphics and Graphic Information

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Interaction II

Maneesh Agrawala

CS 448B: Visualization Fall 2018

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Last Time: Interaction

Gulfs of execution & evaluation

Real world Conceptual model

Evaluation Execution

Gulfs

[Norman 1986]

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[Graphics and Graphic Information Processing, Bertin 81] [Graphics and Graphic Information Processing, Bertin 81]

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Trellis

[Becker, Cleveland, and Shyu 96]

Panel variables

type, yield

Condition variables

location, year

Trellis

[Becker, Cleveland, and Shyu 96]

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Alphabetical ordering Main-effects ordering

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Brushing

■ Interactively select subset of data ■ See selected data in other views ■ Two things (normally views) must be

linked to allow for brushing

Baseball statistics [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

  • f positions

played

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Linking assists to positions GGobi: Brushing

http://www.ggobi.org/

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Dynamic Queries

Query and results

SELECT house FROM east bay WHERE price < 1,000,000 AND bedrooms > 2 ORDER BY price

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Issues

  • 1. For programmers
  • 2. Rigid syntax
  • 3. Only shows exact matches
  • 4. Too few or too many hits
  • 5. No hint on how to reformulate the query
  • 6. Slow question-answer loop
  • 7. Results returned as table

[Ahlberg and Schneiderman 92]

HomeFinder

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Direct manipulation

  • 1. Visual representation of objects and actions
  • 2. Rapid, incremental and reversible actions
  • 3. Selection by pointing (not typing)
  • 4. Immediate and continuous display of

results How quick does in need to be? (rules of thumb)

0.1s: Instantaneous 1.0s: Flow of thought uninterrupted 10s: Keeping user’s attention on dialogue

Announcements

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Assignment 3: Dynamic Queries

1.

Implement interface and produce final writeup

2.

Submit the application and a final writeup on canvas Can work alone or in pairs

Due before class on Oct 29, 2018

Create a small interactive dynamic query application similar to Homefinder, but for SF Restaurant Data.

FilmFinder

[Ahlberg and Schneiderman 93]

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FilmFinder

[Ahlberg and Schneiderman 93]

[Ahlberg and Schneiderman 94]

Alphaslider

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FilmFinder

[Ahlberg and Schneiderman 93]

Zipdecode [from Fry 04]

http://benfry.com/zipdecode/

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NameVoyager

http://www.babynamewizard.com/voyager

TimeSearcher [Hochheiser & Schneiderman 02]

Based on Wattenbergs [2001] idea for sketch-based queries of time-series data.

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3D dynamic queries [Akers et al. 04] 3D dynamic queries [Akers et al. 04]

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Generalized Selection

Visual Queries

Model selections as declarative queries

(-118.371 ≤ lon AND lon ≤ -118.164) AND (33.915 ≤ lat AND lat ≤ 34.089)

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Visual Queries

Model selections as declarative queries

Applicable to dynamic, time-varying data Retarget selection across visual encodings Perform operations on query structure

Select items like this one.

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Generalized Selection

Point to an example and define an abstraction based on one or more properties

[Clark, Brennan]

Blue like this The same shape as that

Abstraction may occur over multiple levels

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Generalized Selection

Provide generalization mechanisms that enable users to expand a selection query along chosen dimensions of interest Expand selections via query relaxation

Interactor Query Builder

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Query Builder

Click: Select Items

(id = China)

Drag: Select Range

(2000 < gni AND gni < 10000) AND (.1 < internet AND internet < .2)

Legend: Select Attributes

(region = The Americas)

Interactor Query Builder Query Visualizer (id = China)

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Interactor Query Builder Query Visualizer (id = China) Interactor Query Builder Query Visualizer (id = China) Query Relaxer

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Interactor Query Builder Query Visualizer (id = China) Query Relaxer

region IN SELECT region FROM data WHERE (id = China) region (region = Asia)

Query Relaxation

Generalize an input query to create an expanded selection, according to:

  • 1. A semantic structure describing the data
  • 2. A traversal policy for that structure
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Time Relaxation Relaxation using Hierarchies

Relax using abstraction hierarchies of the data Traverse in direction of increasing generality Examples A Priori: Calendar, Categories, Geography Data-Driven: Nearest-Neighbor, Clustering

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Relaxation of Networks

Other Input Modalities

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Multi-touch

žTables, wall displays, tablets, whiteboards žDoes is facilitate visual analysis? žWhat affordances are gained/lost?

Kinetica

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Filtering points Filtering points

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Summary

Most visualizations are interactive

■ Even passive media elicit interactions

Good visualizations are task dependent

■ Choose the right space ■ Pick the right interaction technique

Human factors are important

■ Leverage human strengths ■ Assist to get past human limitations