Document Type
Conference Proceeding
Publication Date
6-1-2010
Abstract
Pattern matching on text data has been a fundamental field of Computer Science for nearly 40 years. Databases supporting full-text indexing functionality on text data are now widely used by biologists. In the theoretical literature, the most popular internal-memory index structures are the suffix trees and the suffix arrays, and the most popular external-memory index structure is the string B-tree. However, the practical applicability of these indexes has been limited mainly because of their space consumption and I/O issues. These structures use a lot more space (almost 20 to 50 times more) than the original text data and are often disk-resident. Ferragina and Manzini (2005) and Grossi and Vitter (2005) gave the first compressed text indexes with efficient query times in the internal-memory model. Recently, Chien et al (2008) presented a compact text index in the external memory based on the concept of Geometric Burrows-Wheeler Transform. They also presented lower bounds which suggested that it may be hard to obtain a good index structure in the external memory. In this paper, we investigate this issue from a practical point of view. On the positive side we show an external-memory text indexing structure (based on R-trees and KD-trees) that saves space by about an order of magnitude as compared to the standard String B-tree. While saving space, these structures also maintain a comparable I/O efficiency to that of String B-tree. We also show various space vs I/O efficiency trade-offs for our structures. © 2010 IEEE.
Publication Source (Journal or Book title)
Data Compression Conference Proceedings
First Page
426
Last Page
434
Recommended Citation
Chiu, S., Hon, W., Shah, R., & Vitter, J. (2010). I/O-efficient compressed text indexes: From theory to practice. Data Compression Conference Proceedings, 426-434. https://doi.org/10.1109/DCC.2010.45
