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 -