This project concentrates on research on scaling up this multiple views paradigm for data larger than a million cases, to arbitrary amounts of data. Much of the existing research on working with large data has focused on reducing the size, using binning, or clustering type approaches before rendering the data. This approach may be appropriate for extracting the global trends, but will fail to extract local anomalies and deviations, which might ultimately be more interesting to know about. So we are concentrating our attention on methods to visualize the entire data set. Most of our current work has focused on generating touring plots using movie technology.
Tours are intuitively rotations of high-dimensional data spaces.
The most common implementation is a sequence of 2D projections rendered as a scatterplot, which for 3 variables is simply a 3D rotation. Tours are excellent for exploring for multi-dimensional shapes, embedded subspaces, and the joint distribution of variables. The tour algorithm is especially fast, only depending on the number of cases when taking linear projections. The algorithm is of order n, one pass through the data for each view. We are exploring the use of QuickTime to generate tour movies of the data off-line.