Paper Info
Title
A Compression Scheme for Large Databases
Abstract
Compression of databases not only reduces space requirements but can also reduce overall retrieval times. We have described elsewhere our RAY algorithm for compressing databases containing general-purpose data, such as images, sound, and also text. We describe here an extension to the RAY compression algorithm that permits use on very large databases.In this approach, we build a model based on a small training set and use the model to compress large databases. Our preliminary implementation is slow for compression, but only slightly slower in decompression speed than the popular GZIP scheme. Importantly, we show that the compression effectiveness of our approach is excellent and markedly better than the GZIP and COMPRESS algorithms on our test sets.
Year
DOI
Venue
2000
10.1109/ADC.2000.819807
Australasian Database Conference
Keywords
Field
DocType
compress large databases,general-purpose data,popular gzip scheme,large databases,compression effectiveness,compressing databases,ray algorithm,compress algorithm,decompression speed,compression scheme,ray compression algorithm,database systems,compression algorithms,computer science,very large database,information retrieval,huffman coding,arithmetic,testing,data compression,compression algorithm
Training set,Compression (physics),Data mining,Space requirements,Data compression ratio,Computer science,Huffman coding,Data compression,Database,Lossless compression,Fold (higher-order function)
Conference
ISBN
Citations 
PageRank 
0-7695-0528-7
8
0.55
References 
Authors
7
2
Name
Order
Citations
PageRank
Adam Cannane1887.88
Hugh E. Williams2104893.45