Transcript Intro

CS 5764
Information Visualization
Dr. Chris North
Purvi Saraiya GTA
Today
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What is Information Visualization?
Who cares?
What will I learn?
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
Web, scientific data
news, products sales
shopping
census data
Data
system logsj
sports
Vision: 100MB/sec
Aural: 100KB/sec
Smell:
Haptics
Taste
esp
Human
Data Transfer
How?
Human Vision
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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?
College Degree %
Per Capita Income
%
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
Human
Data Transfer
Data
visualization
The Bigger Problem
Human
Data Transfer
Data
interactive
visualization
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:
Excel can do this
• Min, max, average, %
• These only involve 1 data item or value
• Hard stuff:
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Patterns, trends, distributions, changes over time,
outliers, exceptions,
relationships, correlations, multi-way,
combined min/max, tradeoffs,
clusters, groups, comparisons, context,
anomalies, data errors,
Visualization can do this!
Paths, …
More than just “data transfer”
• Glean higher level knowledge from the data
Learn = data  knowledge
• Hides data
• Hampers knowledge
• Nothing learned
• No insight
• Reveals data
• Reveals knowledge that is
not necessarily “stored”
in the data
• Insight!
Class Motto
Show me
the data!
2. Who Cares?
Presentation is everything
My Philosophy: Optimization
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
Visualization = the best of both
Impressive computation + impressive cognition
3. What Will I Learn?
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Design interactive visualizations
Critique existing designs and tools
Develop visualization software
Empirically evaluate designs
Understand current state-of-art
An HCI focus
• A visualization = a user interface for data
Topics
Information Types:
• Multi-D
• 1D, 2D, 3D spatial
• Hierarchies/Trees
• Networks/Graphs
• Document collections
Strategies:
• Design Principles
• Interaction strategies
• Navigation strategies
• Visual Overviews
• Multiple Views
• Empirical Evaluation
• Development
• Theory
• High-Resolution Displays
GigaPixel Display
Related Courses
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Scientific Visualization (ESM4714)
Computer Graphics (4204, 6xxx)
Usability Engineering (5714)
Research Methods (5014)
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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: (See Syllabus online)
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60%
30%
5%
5%
Project
Homeworks
Paper presentation or review
Experiment & class participation
• Format:
• Read research papers (see web site)
• In-class discussion
• Emphasis on project
Research Class
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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
• Visualization for Intelligence Analysis
• Milestones:
• Team: choose team (due Wed!)
• Design Concept & Presentation: problem, lit.
review, design, schedule (4 weeks)
• Formative Eval & Initial Impl
• Final presentation: final results
• Final paper: publishable?
Paper Presentations
• 10-15 minutes
• Read paper, Present visualization
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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:
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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.
To Do …
• Read: CMS chapter 1 handout (pg 1-16)
• HW 1, due next Mon: SequoiaView
• Form project teams
• Wed: Intell Analysis exercise & Projects