
arXiv: 2004.13389
In the problem of the longest common substring with $k$ mismatches we are given two strings $X, Y$ and must find the maximal length $\ell$ such that there is a length-$\ell$ substring of $X$ and a length-$\ell$ substring of $Y$ that differ in at most $k$ positions. The length $\ell$ can be used as a robust measure of similarity between $X, Y$. In this work, we develop new approximation algorithms for computing $\ell$ that are significantly more efficient that previously known solutions from the theoretical point of view. Our approach is simple and practical, which we confirm via an experimental evaluation, and is probably close to optimal as we demonstrate via a conditional lower bound.
2012 ACM Subject Classification Theory of computation → Pattern matching phrases approximation algorithms, FOS: Computer and information sciences, 2012 ACM Subject Classification Theory of computation → Pattern matching phrases approximation algorithms string similarity LSH conditional lower bounds, [INFO] Computer Science [cs], 004, conditional lower bounds, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), approximation algorithms, string similarity, LSH, ddc: ddc:004
2012 ACM Subject Classification Theory of computation → Pattern matching phrases approximation algorithms, FOS: Computer and information sciences, 2012 ACM Subject Classification Theory of computation → Pattern matching phrases approximation algorithms string similarity LSH conditional lower bounds, [INFO] Computer Science [cs], 004, conditional lower bounds, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), approximation algorithms, string similarity, LSH, ddc: ddc:004
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
