Prof. Bederson CMSC 838B - Information Visualization - Spring 2003



What is information visualization? How is it related to scientific visualization? How does it combine with data mining? Information visualization is emerging as an important fusion of graphics, scientific visualization, database, and human-computer interaction. Dozens of innovative visualizations for 1-, 2-, 3-, and multi-dimensional data have been proposed, along with creative designs for temporal, hierarchical, and network data. This seminar will examine the design alternatives (fisheye, overviews, dynamic queries, etc.), algorithms and data structures, plus human factors evaluations of efficacy for a variety of tasks and users.

This is a research-oriented course. You will learn about the state of the art in Information Visualization by reading and critiquing research papers from the field. You will also contribute to the state of the art by doing original research for your semester project.

Classes will consist of an introduction by the instructor, followed by presentations by students about the assigned readings, and discussion amongst the class. We will make heavy use of visual materials such as videos and live demos of visualization systems. Note that your participation in class discussion is part of your grade.


Text: Readings In Information Visualization: Using Vision to Think, Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman, Morgan Kaufmann Publishers, San Francisco, January 1999, 686 pages, ISBN 1-55860-533-9

Recommended Text:

  • Information Visualization by Robert Spence, ACM Press, Hardcover - 206 pages 1st edition.  (December 2000) Addison-Wesley Pub Co; ISBN: 0201596261


Readings: Each class will include a substantial amount of time spent discussing papers.  We will start each paper discussion by having a student picked at random introduce the paper.  This means that every student must be prepared to discuss every paper every class.

Homeworks: Small homework assignments will be design oriented, giving you an opportunity to quickly sketch new visualization ideas. You will also make use of some existing visualization tools.

Group project: The bulk of this course will concern the semester projects. You will work in small groups on original research. Your goal will be to identify an open problem and develop a solution. For most groups this will involve developing a new visualization software tool or augmenting an existing tool. For students without programming experience, the project could be an evaluation or empirical user study to compare visualizations or test a theory. One good possible project is to design a solution to the InfoVis 2003 Contest. I will provide a list of project ideas that you can choose from, or you can invent your own. I strongly encourage you to work on a problem this is relevant to other work you are doing (e.g. in your thesis research or other classes), or for a faculty member. You will also write a high-quality paper on your project that you will submit to a conference. At the end of the semester you will present your project to the class. For newer students, this project can provide an excellent starting point for your own thesis work.


All assignments can be done on the machines of your choice in the language of your choice, although I recommend using either Java or C#. You are welcome to do the work on a home computer if you have one.  You will get a special WAM account for this class, and can use any WAM machine on campus for your Java work, although there may not be WAM machines that support C#.


All assignments are due at the beginning of the class on the day that they are due. The paper part (if any) must be turned in at class, and the electronic part (if any) must be submitted by the time of the beginning of class. Late assignments will be strictly penalized. Exceptional circumstances will be considered only if discussed with me in advance. All late assignments will have points deducted as follows:

-20% Up to 24 hours late
-50% Up to 48 hours late
-100% More than 48 hours late

Your final grade will be computed using the following contributions:

15% Class participation/Readings
15% Homeworks
20% Midterm
50% Final project

Academic Honesty

All individual assignments/exams must be done on your own. If you are found to cheat by inappropriately sharing your solution with other students, copying others work, etc. you will get an immediate F for the course, and your case will be sent to the university’s Office of Judicial Programs.

For individual and group projects, you are welcome to use all public information to assist you in your work, including books, papers, magazines, websites, etc.  However, in all cases, you must cite the source of any material that you use that is not your own, or I will consider it to be cheating, and will respond to that as above.