Using Bitmap Index for Interactive Exploration of Large Datasets

Many scientific applications generate large spatiotemporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions are defined as contiguous sets of points whose attributes satisfy some user defined conditions, e.g. high temperature regions in a combustion simulation. At each time step, the regions of interest may be identified by first searching for all points that satisfy the conditions and then grouping the points into connected regions. To speed up this process, the searching step may use a tree based indexing scheme, such as a kd-tree or an octree. However, these indices are efficient only if the searches are limited to one or a small number of selected attributes. Scientific datasets often contain hundreds of attributes and scientists frequently study these attributes in complex combinations, e.g. finding regions of high temperature yet low shear rate and pressure. Bitmap indexing is an efficient method for ...

• Type: Conference
• Year: 2003
• Where: SSDBM
• Authors: Kesheng Wu, Wendy S. Koegler, Jacqueline Chen, Arie Shoshani
• Keywords: Connected Regions, Database, Large Spatiotemporal Datasets, Many Scientific Applications, SSDBM 2003