Computing similarity of run-length encoded strings with affine gap penalty

Jin Wook Kim; Amihood Amir; Gad M. Landau; Kunsoo Park

doi:10.1007/11575832_35

Computing similarity of run-length encoded strings with affine gap penalty

Jin Wook Kim, Amihood Amir, Gad M. Landau, Kunsoo Park

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The problem of computing similarity of two run-length encoded strings has been studied for various scoring metrics. Many algorithms have been developed for the longest common subsequence metric and some algorithms for the Levenshtein distance metric and the weighted edit distance metric. In this paper we consider similarity based on the affine gap penalty metric which is a more general and rather complicated scoring metric than the weighted edit distance. To compute similarity in this model efficiently, we convert the problem to a path problem on a directed acyclic graph and use some properties of maximum paths in this graph. We present an O(nm′ + n′m) time algorithm for computing similarity of two run-length encoded strings in the affine gap penalty model, where n′ and m′ are the lengths of given two runlength encoded strings, and n and m are the decoded lengths of given two strings, respectively.

Original language	English
Title of host publication	String Processing and Information Retrieval - 12th International Conference, SPIRE 2005, Proceedings
Publisher	Springer Verlag
Pages	315-326
Number of pages	12
ISBN (Print)	3540297405, 9783540297406
DOIs	https://doi.org/10.1007/11575832_35
State	Published - 2005
Event	12th International Conference on String Processing and Information Retrieval, SPIRE 2005 - Buenos Aires, Argentina Duration: 2 Nov 2005 → 4 Nov 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3772 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on String Processing and Information Retrieval, SPIRE 2005
Country/Territory	Argentina
City	Buenos Aires
Period	2/11/05 → 4/11/05

Bibliographical note

Funding Information:
I K. Park’s work was supported by FPR05A2-341 of 21C Frontier Functional Proteomics Project from Korean Ministry of Science & Technology and A. Amir and G. M. Landau’s work was partially supported by the Israel Science Foundation grant 35/05. ∗Corresponding author. Tel.: +82 2 880 8381; fax: +82 2 885 3141. E-mail address: [email protected] (K. Park).

Funding

I K. Park’s work was supported by FPR05A2-341 of 21C Frontier Functional Proteomics Project from Korean Ministry of Science & Technology and A. Amir and G. M. Landau’s work was partially supported by the Israel Science Foundation grant 35/05. ∗Corresponding author. Tel.: +82 2 880 8381; fax: +82 2 885 3141. E-mail address: [email protected] (K. Park).

Funders	Funder number
Korean Ministry of Science & Technology
Israel Science Foundation	35/05

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10.1007/11575832_35

Cite this

Kim, J. W., Amir, A., Landau, G. M., & Park, K. (2005). Computing similarity of run-length encoded strings with affine gap penalty. In String Processing and Information Retrieval - 12th International Conference, SPIRE 2005, Proceedings (pp. 315-326). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3772 LNCS). Springer Verlag. https://doi.org/10.1007/11575832_35

Kim, Jin Wook ; Amir, Amihood ; Landau, Gad M. et al. / Computing similarity of run-length encoded strings with affine gap penalty. String Processing and Information Retrieval - 12th International Conference, SPIRE 2005, Proceedings. Springer Verlag, 2005. pp. 315-326 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The problem of computing similarity of two run-length encoded strings has been studied for various scoring metrics. Many algorithms have been developed for the longest common subsequence metric and some algorithms for the Levenshtein distance metric and the weighted edit distance metric. In this paper we consider similarity based on the affine gap penalty metric which is a more general and rather complicated scoring metric than the weighted edit distance. To compute similarity in this model efficiently, we convert the problem to a path problem on a directed acyclic graph and use some properties of maximum paths in this graph. We present an O(nm′ + n′m) time algorithm for computing similarity of two run-length encoded strings in the affine gap penalty model, where n′ and m′ are the lengths of given two runlength encoded strings, and n and m are the decoded lengths of given two strings, respectively.",

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Kim, JW, Amir, A, Landau, GM & Park, K 2005, Computing similarity of run-length encoded strings with affine gap penalty. in String Processing and Information Retrieval - 12th International Conference, SPIRE 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3772 LNCS, Springer Verlag, pp. 315-326, 12th International Conference on String Processing and Information Retrieval, SPIRE 2005, Buenos Aires, Argentina, 2/11/05. https://doi.org/10.1007/11575832_35

Computing similarity of run-length encoded strings with affine gap penalty. / Kim, Jin Wook; Amir, Amihood; Landau, Gad M. et al.
String Processing and Information Retrieval - 12th International Conference, SPIRE 2005, Proceedings. Springer Verlag, 2005. p. 315-326 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3772 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Kim JW, Amir A, Landau GM, Park K. Computing similarity of run-length encoded strings with affine gap penalty. In String Processing and Information Retrieval - 12th International Conference, SPIRE 2005, Proceedings. Springer Verlag. 2005. p. 315-326. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11575832_35

Computing similarity of run-length encoded strings with affine gap penalty

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