Abstract :
A practical scheme for maintaining an index for a sliding window in optimal time and space, by use of a suffix tree, is presented. The index supports location of the longest matching substring in time proportional to the length of the match. The total time for build and update operations is proportional to the size of the input. The algorithm, which is simple and straightforward, is presented in detail. The most prominent lossless data compression scheme, when considering compression performance, is prediction by partial matching with unbounded context lengths (PPM). However, previously presented algorithms are hardly practical, considering their extensive use of computational resources. We show that our scheme can be applied to PPM-style compression, obtaining an algorithm that runs in linear time, and in space bounded by an arbitrarily chosen window size. Application to Ziv-Lempel (1977) compression methods is straightforward and the resulting algorithm runs in linear time
Keywords :
data compression; prediction theory; string matching; trees (mathematics); Ziv-Lempel compression methods; algorithm; compression performance; data compression; longest matching substring; lossless data compression; prediction by partial matching; sliding window; suffix trees; unbounded context lengths; Computer science; Context modeling; Costs; Data compression; Performance loss; Tree data structures;
