Learning Modules
Most information visualizations are highly interactive. While a number of excellent textbooks exist, the two-dimensional printouts on paper often cannot convey their true visual appearance and interactive performance. Several textbooks come with accompanying web sites that contain snapshots of user interfaces as well as animations and movies. However, none of them facilitates the exploration, application, evaluation, and comparison of algorithms.
This web page will provide access to a number of learning modules. Each learning module comes with an:
- Description of the data analysis and visualization task
- Usage hints on how to run and use a particular algorithm or tool
- Learning task - a challenging scenario to use an algorithm or to analyze and/or visualize a data set.
- Discussion of the results, and
- References to research papers, online demos, (commercial) applications.
- Acknowledgements
 Modules |
We are in the process of designing and testing diverse learning modules. Comments are always welcome.
- Visual Perception Principles
- Time Series Analysis (TimeSearcher vs. Burst)
- Visualizing Tabular Data (GRIDL, Fisheye Table)
- Visualizing Tree Data (Hyperbolic Tree, Radial Tree, Treemap)
- Semantic Data Analysis (LSA vs. Topics)
- Network Analysis & Visualization (Network Analysis Tool & Pajek)
- Error and Attack Tolerance of Networks (Random, Small World and Scale-Free Networks)
- Search Performance of P2P Networks (CAN & PRU, CAN Search & p-rand Breadth-First Search)
- Clustering Algorithms (Ward's Algorithm vs. Betweenness Centrality)
- Large Scale Network Visualization (VxInsight)
- Interaction and Distortion Techniques (Hyperbolic Tree, Fisheye Table)
- Geospatial Visualizations (Chizu)
- Knowledge Domain Visualizations
- Social Visualizations (iUni and the AW toolkit)
Information Visualization Cyberinfrastructure
@ SLIS, Indiana University
Last Modified Aug 8, 2004