TY - JOUR

T1 - Two algorithms for LCS Consecutive Suffix Alignment

AU - Landau, Gad M.

AU - Myers, Eugene

AU - Ziv-Ukelson, Michal

N1 - Funding Information:
* Corresponding author. Fax: +972 4 824 9331. E-mail addresses: [email protected] (G.M. Landau), [email protected] (E. Myers), [email protected] (M. Ziv-Ukelson). 1 Partially supported by NSF grant CCR-0104307, and by the Israel Science Foundation grants 282/01 and 35/05. 2 Fax: +510 643 8443. 3 Partially supported by the Aly Kaufman Post Doctoral Fellowship and by the Bar-Nir Bergreen Software Technology Center of Excellence.

PY - 2007/1/1

Y1 - 2007/1/1

N2 - The problem of aligning two sequences A and B to determine their similarity is one of the fundamental problems in pattern matching. A challenging, basic variation of the sequence similarity problem is the incremental string comparison problem, denoted Consecutive Suffix Alignment, which is, given two strings A and B, to compute the alignment solution of each suffix of A versus B. Here, we present two solutions to the Consecutive Suffix Alignment Problem under the LCS (Longest Common Subsequence) metric, where the LCS metric measures the subsequence of maximal length common to A and B. The first solution is an O (n L) time and space algorithm for constant alphabets, where the size of the compared strings is O (n) and L ≤ n denotes the size of the LCS of A and B. The second solution is an O (n L + n log | Σ |) time and O (n) space algorithm for general alphabets, where Σ denotes the alphabet of the compared strings.

AB - The problem of aligning two sequences A and B to determine their similarity is one of the fundamental problems in pattern matching. A challenging, basic variation of the sequence similarity problem is the incremental string comparison problem, denoted Consecutive Suffix Alignment, which is, given two strings A and B, to compute the alignment solution of each suffix of A versus B. Here, we present two solutions to the Consecutive Suffix Alignment Problem under the LCS (Longest Common Subsequence) metric, where the LCS metric measures the subsequence of maximal length common to A and B. The first solution is an O (n L) time and space algorithm for constant alphabets, where the size of the compared strings is O (n) and L ≤ n denotes the size of the LCS of A and B. The second solution is an O (n L + n log | Σ |) time and O (n) space algorithm for general alphabets, where Σ denotes the alphabet of the compared strings.

KW - Dynamic programming

KW - Incremental algorithms

KW - Longest common subsequence

KW - Match point arithmetic

UR - http://www.scopus.com/inward/record.url?scp=34547138961&partnerID=8YFLogxK

U2 - 10.1016/j.jcss.2007.03.004

DO - 10.1016/j.jcss.2007.03.004

M3 - Article

AN - SCOPUS:34547138961

SN - 0022-0000

VL - 73

SP - 1095

EP - 1117

JO - Journal of Computer and System Sciences

JF - Journal of Computer and System Sciences

IS - 7

ER -