Compressing Bitmap Indices by Data Reorganization

Many scientific applications generate massive volumes of data through observations or computer simulations, bringing up the need for effective indexing methods for efficient storage and retrieval of scientific data. Unlike conventional databases, scientific data is mostly read-only and its volume can reach to the order of petabytes, making a compact index structure vital. Bitmap indexing has been successfully applied to scientific databases by exploiting the fact that scientific data are enumerated or numerical. Bitmap indices can be compressed with variants of run length encoding for a compact index structure. However even this may not be enough for the enormous data generated in some applications such as high energy physics. In this paper, we study how to reorganize bitmap tables for improved compression rates. Our algorithms are used just as a preprocessing step, thus there is no need to revise the current indexing techniques and the query processing algorithms. We introduce the tu...

• Type: Conference
• Year: 2005
• Where: ICDE
• Authors: Ali Pinar, Tao Tao, Hakan Ferhatosmanoglu
• Keywords: Compact Index Structure, Database, ICDE 2005, Scientific Data, Tuple Reordering Problem