1. applying a known technique to a new application with requirements similar to well-developed applications and
2. developing a new technique from scratch for a new application with unique characteristics.
1. Problem description
2. Data preprocessing
3. Solution formulation
5. Testing, evaluation, validation
We operated the visualization system at several exercises and incorporated the users' feedback into subsequent versions. Some exercises required the system to be operated only by military personnel, so we had to make the system reliable and easy to use. The exercise control staff used JOVE to resolve conflicts about the location of airplanes and submarines. The playback facility allowed participants to review significant events after the action occurred.
At this stage we didn't know what to expect, so we decided to try out a variety of techniques and make our decisions along the way. Each visualization method taught us something new about configuration, quality, and distribution of the data.
With the new term "volume data mining," we would like to say that the proposed method could enrich the traditional volume visualization techniques, to provide the scientists and developers with the "serendipity" for visual exploration of large-scale volume data sets. It is just like a benevolent wizard living in the mountains who can [point] with his magic cane to let the village people know the first position to dig, where an unknown fountain never fails to appear.
David Kao works in the Data Analysis group of the Numerical Aerospace Simulations (NAS) Systems Division at NASA Ames Research Center. His research interests include numerical flow visualization, scientific visualization, computer graphics, and NASA's Earth Observing System (EOS) data visualization.Kao is an adjunct faculty member in the Computer Engineering Department at Santa Clara University and is a research advisor for the National Research Council. He served as a case studies co-chair for the IEEE Visualization conferences in 1999 and 2000. He received his PhD in computer science from Arizona State University in 1991.
Kwan-Liu Ma is an associate professor of computer science at the University of California, Davis, where he teaches and conducts research in the areas of computer graphics and scientific visualization. His career research goal is to improve the overall experience and performance of data visualization through more effective user interface designs, interaction techniques, and high-performance computing.Ma received his PhD from the University of Utah in 1993. He served as co-chair for the 1997 IEEE Symposium on Parallel Rendering, the IEEE Visualization Conference Case Studies in 1998 and 1999, and the first NSF/DOE Workshop on Large Data Visualization.