Pattern matching in LZW compressed files

Compressed pattern matching is an emerging research area that addresses the following problem: Given a text file in compressed format and a pattern, report the occurrence(s) of the pattern in the file with minimal ( or no) decompression. In this paper, we report our work on compressed pattern matching in LZW compressed files. The work includes an extension of Amir et al.'s well-known "almost-optimal" algorithm. The original Amir et al.'s algorithm has been improved to search not only the first occurrence of the pattern but also all other occurrences. A faster implementation for so-called "simple patterns" is also proposed. The work also includes a novel multiple-pattern matching algorithm using the Aho-Corasick algorithm. The algorithm takes O(mt+n+r) time with O(mt) extra space, where n is the size of the compressed file, m is the total length of all patterns, t is the size of the LZW trie, and r is the number of occurrences of the patterns. Extensive experiments have been conducted to test the performance of our algorithms and to compare with other well-known compressed pattern matching algorithms, particularly the BWT-based algorithms and another similar multiple-pattern matching algorithm by Kida et al. that also uses the Aho-Corasick algorithm on the LZW compressed data. The results showed that our multiple-pattern matching algorithm is competitive among the best compressed pattern-matching algorithms and is practically the fastest among all approaches when the number of patterns is not very large. Therefore, our algorithm is preferable for general string matching applications. The proposed algorithm is efficient for large files and it is particularly efficient when being applied on archive search if the archives are compressed with a common LZW trie. LZW is one of the most efficient and popular compression algorithms used extensively and our method requires no modification on the compression algorithm. The work reported in this paper, therefore, has great economic and market potential.