Data compression can be used to simultaneously reduce memory, communication and computation requirements of string comparison. In this paper we address the problem of computing the length of the longest common subsequence (LCS) between run-length-encoded (RLE) strings. We exploit RLE both to reduce the complexity of LCS computation from O(M×N) to O(mN+Mn−mn), where M and N are the lengths of the original strings and m and n the number of runs in their RLE representation, and to improve the inherent parallelism of the proposed algorithm, so that it may execute in O(m+n) steps on a systolic array of M+N units. We also discuss the application of the proposed algorithm to the related problem of edit distance (ED) computation.
Longest Common Subsequence between Run-Length-Encoded Strings: a New Algorithm with Improved Parallelism
FRESCHI, VALERIO;BOGLIOLO, ALESSANDRO
2004
Abstract
Data compression can be used to simultaneously reduce memory, communication and computation requirements of string comparison. In this paper we address the problem of computing the length of the longest common subsequence (LCS) between run-length-encoded (RLE) strings. We exploit RLE both to reduce the complexity of LCS computation from O(M×N) to O(mN+Mn−mn), where M and N are the lengths of the original strings and m and n the number of runs in their RLE representation, and to improve the inherent parallelism of the proposed algorithm, so that it may execute in O(m+n) steps on a systolic array of M+N units. We also discuss the application of the proposed algorithm to the related problem of edit distance (ED) computation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
