Abstract

Data abstraction techniques are widely used in multiresolution visualization sys-tems to reduce visual clutter and facilitate analysis from overview to detail. How-ever, analysts are usually unaware of how well the abstracted data represent the original dataset, which can impact the reliability of results gleaned from the ab-stractions. In this thesis, we define three types of data abstraction quality measures for computing the degree to which the abstraction conveys the original dataset: the Histogram Difference Measure, the Nearest Neighbor Measure and Statistical Measure. They have been integrated within XmdvTool, a public-domain multiresolution visualization system for multivariate data analysis that supports sampling as well as clustering to simplify data. Several interactive operations are provided, including adjusting the data abstraction level, changing selected regions, and setting the ac-ceptable data abstraction quality level. Conducting these operations, analysts can select an optimal data abstraction level.

Citations