Dynamic RLE-Compressed Edit Distance Tables Under General Weighted Cost Functions

Kim and Park [A dynamic edit distance table, J. Disc. Algo., 2:302–312, 2004] proposed a method (KP) based on a “dynamic edit distance table” that allows one to efficiently maintain unit cost edit distance information between two strings A of length m and B of length n when the strings can be modified by single-character edits to their left or right ends. This type of computation is useful e.g. in cyclic string comparison. KP uses linear time, O(m + n), to update the distance representation after each single edit. Recently Hyyro et al. [Incremental string comparison, J. Disc. Algo., 34:2-17, 2015] presented an efficient method for maintaining the dynamic edit distance table under general weighted edit distance, running in O(c(m + n)) time per single edit, where c is the maximum weight of the cost function. The work noted that the Θ(mn) space requirement, and not the running time, may be the main bottleneck in using the dynamic edit distance table. In this paper we take the first steps towards reducing the...

[1]  Jeanette P. Schmidt,et al.  All Highest Scoring Paths in Weighted Grid Graphs and Their Application to Finding All Approximate Repeats in Strings , 1998, SIAM J. Comput..

[2]  Shunsuke Inenaga,et al.  Compacting a Dynamic Edit Distance Table by RLE Compression , 2016, SOFSEM.

[3]  Costas S. Iliopoulos,et al.  Average-Case Optimal Approximate Circular String Matching , 2014, LATA.

[4]  Gad M. Landau,et al.  Incremental String Comparison , 1998, SIAM J. Comput..

[5]  Gonzalo Navarro,et al.  Approximate Matching of Run-Length Compressed Strings , 2001, CPM.

[6]  Kuan-Yu Chen,et al.  A Fully Compressed Algorithm for Computing the Edit Distance of Run-Length Encoded Strings , 2011, Algorithmica.

[7]  Hsing-Yen Ann,et al.  A fast and simple algorithm for computing the longest common subsequence of run-length encoded strings , 2008, Inf. Process. Lett..

[8]  Kuan-Yu Chen,et al.  Finding All Approximate Gapped Palindromes , 2009, ISAAC.

[9]  Robert E. Tarjan,et al.  Deques with Heap Order , 1986, Inf. Process. Lett..

[10]  János Csirik,et al.  An Improved Algorithm for Computing the Edit Distance of Run-Length Coded Strings , 1995, Inf. Process. Lett..

[11]  Gad M. Landau,et al.  Edit distance of run-length encoded strings , 2002, Inf. Process. Lett..

[12]  Yoshifumi Sakai Computing the Longest Common Subsequence of Two Run-Length Encoded Strings , 2012, ISAAC.

[13]  Alexander Tiskin,et al.  String comparison by transposition networks , 2009, ArXiv.

[14]  Yue-Li Wang,et al.  Sequence Alignment Algorithms for Run-Length-Encoded Strings , 2008, COCOON.

[15]  Gad M. Landau,et al.  Matching for Run-Length Encoded Strings , 1999, J. Complex..

[16]  Shunsuke Inenaga,et al.  Dynamic Edit Distance Table under a General Weighted Cost Function , 2010, SOFSEM.

[17]  Sung-Ryul Kim,et al.  A Dynamic Edit Distance Table , 2000, CPM.

[18]  Gonzalo Navarro,et al.  Approximate Matching of Run-Length Compressed Strings , 2002, Algorithmica.