Interaction & Dynamic Queries 1

Interaction &

Dynamic Queries 1

CS 7450 - Information Visualization January 29, 2004

John Stasko

Interaction

• How do you define “interactive”?

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Response Times

• .1 sec - animation, visual continuity, sliders

• 1 sec - system response, conversation break

• 10 sec - cognitive response

Interaction - 3 Transformations

• Data (Primary Focus)

− Selecting cases to show

• Visual

− Selecting a different representation

− UI controls for chart/graph/vis

• View

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Data Transformation

• Selecting what you want to see

• What are some techniques?

Example

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Data Transformation

• Interaction forms:

− Pop-up tooltips (bubble help)

− Direct walk

− (General) Brushing

− Details-on-demand

− Dynamic query

− Histogram brushing

Pop-up tooltips

• Hovering mouse cursor brings up details of item

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Direct Walk

• Linkages between cases

• Exploring one may lead to another

• Example:

− Following hyperlinks on web pages

Brushing

• Applies when you have multiple views of the same data

• Selecting or highlighting a case in one view generates highlighting the case in the other views

• Very common technique in InfoVis

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Brushing

Same item

Details-on-Demand

• May not be showing all the data due to scale problem

− May be showing some abstraction of groups of elements

• Expand set of data to show more details, perhaps individual cases

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Dynamic Query

• Probably most common technique

• Let’s explore more details...

DB Queries

• Query language

− Select house-address From atl-realty-db

Where price >= 200,000 and price <= 400,000 and bathrooms >= 3 and garage == 2 and bedrooms >= 4

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DB Queries

• Pluses?

• Minuses?

Typical Query Response

• 124 hits found

− 1. 748 Oak St. - a beautiful …

− 2. 623 Pine Ave. -

− …

• 0 hits found

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Problems

• Must learn language

• Only shows exact matches

• Don’t know magnitude of results

• No helpful context is shown

• Reformulating to a new query can be slow

• ...

Dynamic Query

• Specifying a query brings immediate display of results

• Responsive interaction (< .1 sec) with data, concurrent presentation of solution

• “Fly through the data”, promote

exploration, make it a much more “live”

experience

− Timesharing vs. batch

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Dynamic Query Constituents

• Visual representation of world of action including both the objects and actions

• Rapid, incremental and reversible actions

• Selection by pointing (not typing)

• Immediate and continuous display of results

From: Ahlberg &

Shneiderman, CHI ‘94

Imperfection

• Idea at heart of Dynamic Query

− There often simply isn’t one perfect response to a query

− Want to understand a set of tradeoffs and choose some “best” compromise

− You may learn more about your problem as you explore

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Software Demo

• HomeFinder - Univ. of Maryland

What Did We See?

• Interface

− buttons

− sliders (nominal --> ordinal)

− alphasliders

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Query Controls

• Variable types

− Binary nominal - Buttons

− Nominal with low cardinality - Radio buttons

− Ordinal, quantitative - sliders

Alphaslider

A B C DEF G HI J K L M N OP QR S T UVW XYZ Goldfinger

Current selection

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Rangeslider

100

Low selection thumb

Real data range

High selection thumb

0

FilmFinder

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Spotfire

Current assignment

Spotfire Features

• Starfield display

• Tight coupling

− features to guide the user

− rapid, incremental, reversible interactions

− display invariants

− continuous display

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Nice Application

www.myrateplan.com/cellphones

DQ Strengths

• ?

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DQ Strengths

• Work is faster

• Promote reversing, undo, exploration

• Very natural interaction

• Shows the data

DQ Weaknesses

• ?

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DQ Weakness

• Can you formulate an arbitrary boolean expression?

− !(A1 V A2) ^ A3 V (A4 V A5 ^ A6) V …

• But do people really do this often?

DQ Weakness

• Controls take space!

− How much in Spotfire?

• Put data in controls...

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Data Visualization Sliders

Low selection thumb

Data distribution

High selection thumb

From: Eick, UIST ‘94

Drawing Queries

Query Sketch

You specify a timeline query by drawing a rough pattern for it, the system brings back near

matches User-drawn query

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DQ Weakness

• As data set gets larger, real-time

interaction becomes increasingly difficult

• Storage - Data structures

− linear array

− grid file

− quad, k-d trees

− bit vectors

Brushing Histograms

• Special case of brushing

• Data values represented in histograms that can be clicked on and selected (controls region)

• When items selected there, the

corresponding item(s) are highlighted in main view windows

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BH Example

DataMaps Maryland

&

Va Tech

DQ vs. BH

• Empirical Study

− Use DataMaps, a geographic (US states) data visualization tool

− Have participants do different tasks with both methods

How many states have pop between x and y in 1970?

Given 3 states, which has the lowest median income?

What’s the relationship between education and income?

Demo

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Findings

• Brushing histograms better and more highly rated for more complex discovery tasks

− Attribute correlation, compare, and trend evaluation

• Dynamic queries better for more simple range specification tasks

− Single range, multiple ranges, multiple criteria Functioned more as its own infovis tool

Functioned more as auxiliary control for other vizs

Case Study

Attribute Explorer

Video

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Fundamental Idea

• Show all the data, use different displays for different variables

• Use brushing so that interaction with one view highlights selections in others

DQ vs. BH

• Fundamental Differences:

− BH highlights data of interest;

DQ filters unwanted data

− DQ does single range query;

BH allows multiple ranges

− DQ users interact with the query (low,hi);

BH users interact directly with data

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Case Study - SDM

• Developed at CMU

• Limitations of static visuals

− Unable to focus on different objects with context

− Clutter and object occlusion

− Objects are dwarfed in the scale for the entire data set

− Fail to classify object sets

− Difficult to compare quantities which are not spatially contiguous

Chuah et al, UIST ‘95

System in Action

Video

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What We Saw

Object handles Maintain context: Body and shell

Viewing occluded objects Interactive augmentation

Interaction

• More to come...

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Upcoming

• Interaction and Dynamic Queries 2

− Reading

Chapter 5 again Fishkin & Stone

• Time Series data

• HW3 due in a week

Sources Used

• Spence, CMS books

• Shneiderman; Ahlberg et al; Chuah et al;

Tweedie et al articles

• Chiu & Wang F99 slides

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Extras

• Additional information

SDM Components

• Object-centered selection

• Dynamic and flexible operations

• SDM handles

• Objects constraints and feedback techniques

• Context persistence

• Set-wide operations

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Advantages

• ?

Advantages

• Focus and context

• View occluded objects

• View different object sets in different scales

• Visualization is not part of the underlying data

• Compare the widths and heights of objects at a distance

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Disadvantages

• ?

Disadvantages

• Object manipulation causes occlusion

• Users cannot operate on parts of objects

• With limited scope, certain objects are occluded by the focus of objects

• Changing the width/height can blur the linear relationships between objects