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Introduction to Information Visualization Lecture Notes for Fall, 2009 Dr. Adrian Rusu rusu@rowan.edu 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 l.jpg
Introduction to Information Visualization

Lecture Notes for Fall, 2009

Dr. Adrian Rusu

rusu@rowan.edu

Robinson 3rd floor

Office Hours: M 3:00PM – 4:00PM,

T 3:00PM-5:00PM


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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)


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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)


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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


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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


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Course Objectives

  • To provide a comprehensive introduction to information visualization

  • To become familiar with a graphics programming language (such as OpenGL)


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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


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What is this course about? list)

  • Techniques and strategies to build systems which better assist analysts to visually analyze information (data)


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Prerequisites list)

  • Linear Algebra (1701.210) or Math for Engineering Analysis (1701.236)

  • Proficiency in programming languages C/C++ and/or Java


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Required Textbook list)

  • Robert Spence. “Information Visualization 2nd Edition": Pearson.


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Recommended Books list)

  • 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.


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Course Web Page list)

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


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Class Discussion Page list)

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


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Add/Drop Policy list)

  • Second week of classes

    • Deadline to add

  • Second week of classes

    • Deadline to drop


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Grading list)

  • 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


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Extra Credit list)

  • 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


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Research Presentation list)

  • 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


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Projects list)

  • 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)


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Attendance Policy list)

  • 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.


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Be Involved… list)

  • 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


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…But Don’t Be Too Involved list)

  • 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


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Assignments (1) list)

  • 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


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Assignments (2) list)

  • 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


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Questions list)

  • 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


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Mandatory Office Visits list)

  • 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.


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Students with Disabilities list)

  • 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.


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Miscellaneous list)

  • 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


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Important Dates list)

  • Election Day (No class): Tuesday 11/03

  • Final Exam (unless announced otherwise): TBA.

  • Final Project Presentations: TBA (in class)


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What is list)Information Visualization?

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

    • Card, Mackinlay, Shneiderman

Human

Data

Data Transfer

How?


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Example 1 list)

  • Relationship between Income and Education?


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College Degree % list)

Per Capita Income


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Example 2 list)

Home

Finder


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More than just “data transfer” list)

  • 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


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User Tasks list)

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!


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