Brushing, Linking & Interactive Querying

Information VisualizationFebruary 15, 2002Sarah Waterson

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.

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.”

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

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

High Interaction Graphics

ClarityInformation only on demand, cleaner & more focused displays, allow a range of options

RobustnessAvoid drawing inferences from only one view

PowerCombine views, leverage exploration

Possibility3+ dimensional data, animation

Principles

1. Simple, easy to interpret views2. Information hiding, details on

demand3. Direct Manipulation

Linking & Brushing

LinkingVisually indicating which parts of one data display correspond to that of another

BrushingAllowing 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

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

Another ExamplePoint Visualization Tool (PVT) of data related by postal codes

Application DomainsSpatial 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 VisualizationVery difficult problem with many dimensions and possible visualizations: the code, data structures, communication, execution threads, debugging, memory management, etc. SeeSoft

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.

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

Examples

Periodic Table of the ElementsAdjust properties with sliders on the bottom to highlight matching elements.

More Examples

DynaMapCervical cancer rates from 1950-1970 - modify year, state, demographics

Unix Directory Exploration

Even More Examples

Yet More Examples

Information Visualization and Exploration Environment (IVEE)

Job to Skills matching

Devise

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?

Home Finder: Map

Home Finder: Text

Film Finder

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…

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

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.

Data Visualization Sliders

Use the sliders themselves as data displays

“Painting” metaphor for specifying disconnected intervals

The Control ProjectContinuous 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

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

Online Aggregation

Online Enumeration – UI

Database analysts vs. Domain experts

Eyeballing in Databases and lists

Using fuzzy techniques, such as the scrollbar

CloudsRender 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

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.

Movable Filters

Movable Magic LensTM filters over starfield displays for multiple simultaneous visual transformations and queries

Enhanced brushing with sliders?

Queries & FiltersBoolean Composition

Semantic Filters

Real-valued Queries

Missing Values

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?

Thoughts

More than MiceInteraction techniques beyond point and click

Understanding the DataUnderstanding the data and model – How to create the interface appropriate for investigation.