class projects:

TreeJuxtaposer: Scalable Tree Comparison using Focus+Context with Guaranteed Visibility
     Nicholas Chen and Maryam Farboodi
Genome Assembly Analytics with Hawkeye     -- video --
     Adam Phillippy & Michael Schatz
Stock Seeker: Finding Correspondences in Stock Data     -- video --
     Vlad Morariu and Abhinav Gupta
Enhancing Set-Analysis through Scalable Visualizations
     Hamid Haidarian Shahri and Mudit Agrawal
CaseCluster: Visualizing Case References between Supreme Court Cases
     Georg Apitz & Neeti Ogale
Patternfinder 3.0: Sparse Temporal Data Visual Query Application
     Ayewah, Nathaniel, Johnson, Gleneesha, Song, Hyunyoung
Using Time Series Analysis to Visualize and Evaluate Background Subtraction Results in Computer Vision Applications
     Samah Ramadan
Adapting LifeLines to Army Officer Personnel Processes     -- video --
      Scott Nestler & Rachel Bradley
Visualizing Search in Networks     -- video --
      Derek Juba, Timur Chabuk, Chang Hu

Term: Spring 2006
class hours: Tues & Thurs 9:30am - 10:45am    Room: CSIC 3118

Professor:     Dr. Ben Shneiderman
email: ben [AT] cs.umd.edu    Phone: 301-405-2680
office hours: Tues & Thurs 11am - noon    Room: AVW 3177


Topics: 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, coordinated views, plus human factors evaluations of efficacy for a variety of tasks and users.

Students will read current literature and conduct collaborative projects to design, implement, and/or evaluate existing or novel visualizations. Mid-term and final exams will be given, so the course qualifies for MS and PhD comps.


Homework: Students will read, present and critique papers. Students will use existing information visualization tools, such as Spotfire, TimeSearcher, HCE, and Treemaps 4.0 to build useful visualizations on data sets, then conduct a major team project to create or extend an information visualization to deal with a realistic problem.


Readings: Key papers will be assigned for reading, including chapters of "Illuminating the Path: The Research and Development Agenda for Visual Analytics" Other sources include:
1) Designing the User Interface, 4th Edition, B. Shneiderman & C. Plaisant, Addison Wesley (2005). Chapter 14.
2) 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,

Grade: Homeworks & Presentations 20%, Midterm 15%, Final 25%, Team project 30%, Participation 10%