Using R-Trees for Interactive Visualization of Large Multidimensional Datasets

Large, multidimensional datasets are difficult to visualize and analyze. Visualization interfaces are constrained in resolution and dimension, so cluttering and problems of projecting many dimensions into the available low dimensions are inherent. Methods of real-time interaction facilitate analysis, but often these are not available due to the computational complexity required to use them. By organizing the dataset into a level-of-detail (LOD) hierarchy, our proposed method solves problems of both inefficient interaction and visual cluttering. We do this by introducing an implementation of R-trees for large multidimensional datasets. We introduce several useful methods for interaction, by queries and refinement, to explain the relevance of interaction and show that it can be done efficiently with R-trees. We examine the applicability of

hierarchical parallel coordinates

to datasets organized within an R-tree, and build upon previous work in

hierarchical star coordinates

to introduce a novel method for visualizing bounding

hyperboxes

of internal R-tree nodes. Finally, we examine two datasets using our proposed method and present and discuss results.