CS 5764Information Visualization

Dr. Chris North

GTA: Beth Yost

Today

1. What is Information Visualization?

2. Who cares?

3. What will I learn?

4. How will I learn it?

1. What is Information Visualization?

• The use of computer-supported, interactive, visual representations of abstract data to amplify cognition– Card, Mackinlay, Shneiderman

The Big Problem

Data

Human

How?

Data Transfer

Web, …

Vision:

Human Vision

• Highest bandwidth sense

• Fast, parallel

• Pattern recognition

• Pre-attentive

• Extends memory and cognitive capacity• (Multiplication test)

• People think visually

• Brain = 8 lbs, vision = 3 lbs

Impressive. Lets use it!

Find the Red Square:

Pre-attentive

• Which state has highest Income? Avg? Distribution?• Relationship between Income and Education?• Outliers?

Per Capita Income

Col

lege

Deg

ree

%

%

Visual Representation Matters!

• Text vs. Graphics

• What if you could only see 1 state’s data at a time? (e.g. Census Bureau’s website)

• What if I read the data to you?

• Graphics vs. Graphics• depends on user tasks, data, …

History: Static Graphics

Minard, 1869

The Big Problem

Data

Human

visualization

Data Transfer

The Bigger Problem

Data

Human

interactivevisualization

Data Transfer

Interactive Graphics

• Homefinder

Search Forms

• Avoid the temptation to design a form-based search engine• More tasks than just “search”

• How do I know what to “search” for?

• What if there’s something better that I don’t know to search for?

• Hides the data• Only supports Q&A

User Tasks

• Easy stuff:• Min, max, average, %• These only involve 1 data item or value

• Hard stuff:• Patterns, trends, distributions, changes over time,• outliers, exceptions, • relationships, correlations, multi-way, • combined min/max, tradeoffs, • clusters, groups, comparisons, context, • anomalies, data errors, • Paths, …

Excel can do this

Visualization can do this!

More than just “data transfer”

• Glean higher level knowledge from the data

Learn = data knowledge

• Reveals data• Reveals knowledge that is not necessarily “stored” in the data• Insight!

• Hides data• Hampers knowledge• Nothing learned• No insight

Class Motto

Show me the data!

2. Who Cares?

Presentation is everything

My Philosophy: Optimization

Visualization = the best of both

Impressive computation + impressive cognition

Computer•Serial•Symbolic•Static•Deterministic•Exact •Binary, 0/1•Computation•Programmed •Follow instructions•Amoral

Human•Parallel •Visual •Dynamic •Non-deterministic •Fuzzy•Gestalt, whole, patterns •Understanding •Free will•Creative •Moral

3. What Will I Learn?

• Design interactive visualizations

• Critique existing designs and tools

• Develop visualization software

• Empirically evaluate designs

An HCI focus• A visualization = a user interface for data

*

Topics

Information Types: • Multi-D• 1D• 2D• 3D• Hierarchies/Trees• Networks/Graphs• Document collections

Strategies:• Design Principles• Interaction strategies• Navigation strategies• Visual Overviews• Multiple Views• Empirical Evaluation• Development• Theory• Tools

Related Courses

• Scientific Visualization (ESM4714)

• Computer Graphics (4204, 6xxx)

• Usability Engineering (5714)

• Research Methods (5014)

• Model & Theories of HCI (5724)• User Interface Software (5774)• Info Storage & Retrieval (5604)• Databases (5614), Digital Libraries (6xxx)• Data Mining (6xxx)

4. How will I learn it?Course Mechanics

• http://infovis.cs.vt.edu/cs5764/

• Grading:• 5% Paper presentation or review

• 20% Homeworks (4)

• 25% Pop quizzes and in-class activities

• 50% Project

• Format:• Read research papers (see web site)

• In-class discussion

• Emphasis on project

Research Class

• Creativity

• Open ended

• Often no “right” answer

• Reasoning/argument is more important

• Thinking deeply

• Self motivation, seek to excel

• Contribute to the state-of-the-art

• Jump start for thesis research, publication

Project• Groups of 3 students• Categories:

• Development: design, implement, evaluate new visualization

• Evaluation: empirical experiments with users• Theory: literature survey, synthesize theory or

taxonomy

• Milestones:• Abstract: choose team and topic (due next week!)• Proposal: problem, lit. review, design, schedule• Mid-semester presentation: initial results• Final presentation: final results• Final paper: publishable?

Presentations

• 10-15 minutes

• Read paper, Present visualization

• Information type

• Visual mappings

• Show pictures / demo / video

• Strengths, weaknesses• E.g. Scale, insight factor, user tasks

Presentations

• Goals:• 1: understand visualization (mappings, simple examples)

• 2: strengths, weaknesses

• Tips:• Time is short: 10-15 min = ~7 slides, practice out loud

• Use pictures, pictures, pictures, pictures, …

• Use text only to hammer key points

• The “slide-sorter” test

• What’s the take-home message? ~2 main points

• Conclude with controversy

• Motivate!

Implementation detail crap• The first step of processing requires the construction of

several tree and graph structures to store the database.• System then builds visualization of data by mapping data

attributes of graph items to graphical attributes of nodes and links in the visualization windows on screen.

• More boring stuff nobody is ever going to read here or if they do they wont understand it anyway so why bother.

• If they do read it then they most certainly will not be listening to what you are saying so why bother give a talk? Why not just sit down and let everybody read your slides or just hand out the paper and then say ‘thank you’.

• This person needs to take Dr. North’s info vis class.

Force Adds?

• Why?

• Academic goals?

• Can you keep up?