html5-img
1 / 36

Introduction to Information Visualization

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)

jacob
Télécharger la présentation

Introduction to Information Visualization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 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

  2. 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)

  3. 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)

  4. 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

  5. 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

  6. Course Objectives • To provide a comprehensive introduction to information visualization • To become familiar with a graphics programming language (such as OpenGL)

  7. 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

  8. What is this course about? • Techniques and strategies to build systems which better assist analysts to visually analyze information (data)

  9. Prerequisites • Linear Algebra (1701.210) or Math for Engineering Analysis (1701.236) • Proficiency in programming languages C/C++ and/or Java

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

  11. 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.

  12. Course Web Page http://elvis.rowan.edu/~rusu/InformationVisualization.html

  13. Class Discussion Page http://webct.rowan.edu – Information Visualization

  14. Add/Drop Policy • Second week of classes • Deadline to add • Second week of classes • Deadline to drop

  15. 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

  16. 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

  17. 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

  18. 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)

  19. 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.

  20. 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

  21. …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

  22. 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

  23. 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

  24. 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

  25. 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.

  26. 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.

  27. 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

  28. Important Dates • Election Day (No class): Tuesday 11/03 • Final Exam (unless announced otherwise): TBA. • Final Project Presentations: TBA (in class)

  29. 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?

  30. Example 1 • Relationship between Income and Education?

  31. College Degree % Per Capita Income

  32. Example 2 Home Finder

  33. 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

  34. 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!

More Related