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Introduction to Information Visualization Lecture Notes for Fall, 2009 Dr. Adrian Rusu [email protected] Robinson 3 rd floor Office Hours: M 3:00PM – 4:00PM, T 3:00PM-5:00PM Undergraduate: Graphics and Visualization Specialization Four or more courses from Linear Algebra (MTH 01.210)

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introduction to information visualization
Introduction to Information Visualization

Lecture Notes for Fall, 2009

Dr. Adrian Rusu

[email protected]

Robinson 3rd floor

Office Hours: M 3:00PM – 4:00PM,

T 3:00PM-5:00PM

undergraduate graphics and visualization specialization
Undergraduate: Graphics and Visualization Specialization
  • Four or more courses from
    • Linear Algebra (MTH 01.210)
    • Data Structures and Algorithms (CS 04.222)
    • Introduction to Computer Graphics (CS 07.360)
    • Introduction to Information Visualization (CS 07.370)
    • Introduction to Computer Animation (CS 07.380)
graduate software engineering certificate
Graduate: Software Engineering Certificate
  • Information Visualization (CS 07.570)
  • Advanced Software Engineering (CS 07.523)
  • Advanced Object Oriented Design (CS 04.570)
  • Programming Languages: Theory, Implementation, and Application (CS 04.548)
graphics visualization animation
Graphics, Visualization, Animation
  • Common Topics
    • Elements of a Graphics System
    • Synthetic Camera Model
    • Graphics Architecture
    • Graphics Programming (OpenGL)
    • Graphics Modeling (Maya)
    • Geometrical Linear Transformations (2D and 3D)
    • Geometric Modeling
graphics visualization animation5
Graphics, Visualization, Animation
  • Graphics Topics
    • Clipping Algorithms (2D and 3D)
    • Types of Projections (of a 3D scene onto a 2D plane)
    • Illumination and Shading Models
    • Visible-Surface Determination Algorithms
    • Transparency
  • Animation Topics
    • Animation Principles
    • Keyframing / Interpolation
    • Rigid Body Dynamics
    • Articulated Figure Motion: forward and inverse kinematics, walking, motion capture
    • Group Behavior: flocking, particle systems
    • Facial Animation
    • Animation of Natural Phenomenon: fire, smoke, plants
    • Animating Surfaces: cloth, hair, fur
course objectives
Course Objectives
  • To provide a comprehensive introduction to information visualization
  • To become familiar with a graphics programming language (such as OpenGL)
information visualization topics to be covered wishful list
Information Visualization topics to be covered (wishful list)
  • Information Visualization Design Principles and Theory
  • Mental Models of Human Beings
  • Color in Information Display
  • Interaction Strategies
  • Multi-dimensional Visualization
  • Zoomable User Interfaces
  • Space and Time Limitations
  • Understanding Relational Data (Graphs and Hierarchies)
  • Visualization Systems Evaluation
what is this course about
What is this course about?
  • Techniques and strategies to build systems which better assist analysts to visually analyze information (data)
prerequisites
Prerequisites
  • Linear Algebra (1701.210) or Math for Engineering Analysis (1701.236)
  • Proficiency in programming languages C/C++ and/or Java
required textbook
Required Textbook
  • Robert Spence. “Information Visualization 2nd Edition": Pearson.
recommended books
Recommended Books
  • Benjamin Bederson and Ben Shneiderman. "The Craft of Information Visualization ": Morgan-Kaufmann.
  • Ben Shneiderman. "Leonardo\'s Laptop: Human Needs and the New Computing Technologies": MIT Press.
  • Daniel McCracken and Rosalee Wolfe. "User-Centered Website Development: A Human-Computer Interaction Approach": Prentice Hall.
  • Giuseppe Di Battista, Peter Eades, Roberto Tamassia, and Ioannis Tollis. "Graph Drawing: Algorithms for the Visualization of Graphs": Prentice Hall.
  • Colin Ware. "Information Visualization: Perception for Design" (2nd Edition): Morgan-Kaufmann.
  • Dave Shreiner. “OpenGL Reference Manual” (4th edition): Addison Wesley.
  • Dave Shreiner, Mason Woo, Jackie Neider, Tom Davis. “OpenGL Programming Guide” (4th edition): Addison Wesley.
course web page
Course Web Page

http://elvis.rowan.edu/~rusu/InformationVisualization.html

class discussion page
Class Discussion Page

http://webct.rowan.edu – Information Visualization

add drop policy
Add/Drop Policy
  • Second week of classes
    • Deadline to add
  • Second week of classes
    • Deadline to drop
slide15

Grading

  • Final Exam (25% Final Exam): Final Exam is comprehensive. Closed book.
  • Homework (18%). No late homework for any reason.
  • Course Involvement and Attendance (qualifies you for Extra Credit - up to 3%)
  • Class Participation, Imagination (virtually unlimited Extra Credit)
  • 2 Mandatory Office Visits (2%)
  • 2 Projects (10% Project1, 35% Project2)
  • Research Presentation (10%)
  • Final grades: 92-100% = A, 88-91.9% = A-, 84-87.9% = B+, 80-83.9% = B, 76-79.9% = B-, 72-75.9% = C+, 68-71.9% = C, 64-67.9% = C-, 60-63.9% = D+, 56-59.9% = D, 52-55.9% = D-, 0-51.9% = F.
  • Always check your (partial) grades
extra credit
Extra Credit
  • The instructor will assign up to 3% extra credit available at the end of the course (if you need it!) for class participation (answering and asking questions) and attendance.
  • Ad-hoc (in-class) extra credit.
  • For assignments:
    • For significant (or smart) improvements
    • Need to check with your instructor first
research presentation
Research Presentation
  • Undergraduate
    • 10-15 minutes presentation of a conference paper on information visualization topics
  • Graduate
    • 30-35 minutes presentation
    • Survey report
      • In-depth study into an area of visualization
      • Survey the state of the art via summary of journal/conference papers
    • Technique report
      • Study a particular technique in depth
projects
Projects
  • Purpose of the projects
    • Project 1: Hands on experience with graphics programming
    • Project 2: Hands on experience in designing and implementing an information visualization system
  • Group projects
    • Accepted and encouraged
    • Work must reflect number of members in a group
  • Demo / Presentation
    • Show off your visualization systems
    • Oral summary of your report
      • Use visuals (Powerpoint, HTML, PDFs)
attendance policy
Attendance Policy
  • As a student at Rowan University, you are expected to attend all classes.
  • Class attendance will be taken at the beginning of each lecture.
  • A zero grade will be issued if you miss an exam, unless you inform your instructor beforehand and you can present a documented excuse.
  • Excessive absences (as judged by the instructor) may lower your grade.
  • Students who miss more than 4 meetings will be reported to the Dean of Students and will receive an F in the course.
be involved
Be Involved…
  • Attend class
    • Much is covered that is not in the textbook or in the lecture notes
      • Material is core part of the exams
    • Official place for announcements
  • Visit course Web site on a regular basis
    • Lecture Notes
    • Assignments
  • Use office hours
  • Ask questions
but don t be too involved
…But Don’t Be Too Involved
  • You cheat, you fail!
    • Final grade is “F”, irrespective of partial grades
    • Homework, project, exams
  • To avoid being a cheater
    • Always do your work by yourself
    • Do not borrow work (not even from the Web)
    • Do not lend work
      • Do not put your work on the Web
      • For programming assignments, allowing others to look at your code is expressly forbidden
  • Your professor is your friend, but your friend is not your professor
    • Your friend’s help may be cheating
assignments 1
Assignments (1)
  • Hand in on time
    • You do get sufficient time
    • Start early
    • Do not wait until the last minute
      • Assignments take time
      • Printers break, paper runs out
      • You are not the only one
  • No late assignments
assignments 2
Assignments (2)
  • Package properly
    • Every assignment…
      • …lists your name
      • …lists the course number
      • …has a cover page
      • …is properly stapled
    • No handwriting
    • Disks (when needed) are properly attached
    • Failure results in loss of points
questions
Questions
  • When in doubt
    • Ask your professor
      • Open door policy
      • Questions during lecture are especially encouraged
  • Post questions on the discussion page (preferred)
  • E-mail questions
  • Questions will generally be answered within 24 hours (except weekends)
    • So don’t leave your questions to the day before an assignment is due
mandatory office visits
Mandatory Office Visits
  • The first mandatory office visit must occur during the first week of the semester in order for you to receive credit for it.
  • You must also fill out and hand in the questionnaire (found at the class Web page) at the time of your first mandatory visit.
  • The second mandatory office visit must occur mid-semester.
  • Note: submit your filled questionnaires by email and only come to the office if asked by the instructor.
students with disabilities
Students with Disabilities
  • Students with disabilities are encouraged to speak with me as early in the semester as possible about their needs for special accommodations. 
  • Late notification will delay requested accommodations.
miscellaneous
Miscellaneous
  • You get out of the course what you put into it
    • Follow instructions
    • Read and study the textbook and notes
    • Help is available, do not be afraid to ask questions
    • Discover programming details by yourself
important dates
Important Dates
  • Election Day (No class): Tuesday 11/03
  • Final Exam (unless announced otherwise): TBA.
  • Final Project Presentations: TBA (in class)
what is information visualization
What is Information Visualization?
  • The use of computer-supported, interactive, visual representations of abstract data to amplify cognition
    • Card, Mackinlay, Shneiderman

Human

Data

Data Transfer

How?

example 1
Example 1
  • Relationship between Income and Education?
slide31

College Degree %

Per Capita Income

example 2
Example 2

Home

Finder

more than just data transfer
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
user tasks
User Tasks

Excel can do this

  • 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,

Visualization can do this!