Fast algorithms for finding nearest common ancestors
SIAM Journal on Computing
Data compression: methods and theory
Data compression: methods and theory
Block edit models for approximate string matching
Theoretical Computer Science - Special issue: Latin American theoretical informatics
Approximate nearest neighbors and sequence comparison with block operations
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
The string-to-string correction problem with block moves
ACM Transactions on Computer Systems (TOCS)
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Rapid identification of repeated patterns in strings, trees and arrays
STOC '72 Proceedings of the fourth annual ACM symposium on Theory of computing
Linear pattern matching algorithms
SWAT '73 Proceedings of the 14th Annual Symposium on Switching and Automata Theory (swat 1973)
An efficient algorithm for sequence comparison with block reversals
Theoretical Computer Science - Latin American theorotical informatics
A space-efficient algorithm for sequence alignment with inversions and reversals
Theoretical Computer Science - Special papers from: COCOON 2003
Distance measures for biological sequences: Some recent approaches
International Journal of Approximate Reasoning
A space efficient algorithm for sequence alignment with inversions
COCOON'03 Proceedings of the 9th annual international conference on Computing and combinatorics
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Given two sequences X and Y that are strings over some alphabet set, we consider the distance d(X, Y ) between them defined to be minimum number of character replacements and block (substring) reversals needed to transform X to Y (or vice versa). This is the "simplest" sequence comparison problem we know of that allows natural block edit operations. Block reversals arise naturally in genomic sequence comparison; they are also of interest in matching music data. We present an improved algorithm for exactly computing the distance d(X, Y ); it takes time O(|X| log2 |X|), and hence, is near-linear. Trivial approach takes quadratic time and the best known previous algorithm for this problem takes time 驴(|X| log3 |X|).