A compression scheme for large databases
Adam Cannane, Hugh E. Williams · 2002
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.