$LCSk$++: Practical similarity metric for long strings

In this paper we present LCSk++: a new metric for measuring the similarity of long strings, and provide an algorithm for its efficient computation. With ever increasing size of strings occuring in practice, e.g. large genomes of plants and animals, classic algorithms such as Longest Common Subsequence (LCS) fail due to demanding computational complexity. Recently, Benson et al. defined a similarity metric named LCSk. By relaxing the requirement that the k-length substrings should not overlap, we extend their definition into a new metric. An efficient algorithm is presented which computes LCSk++ with complexity of O((|X| + |Y |) log(|X| + |Y |)) for strings X and Y under a realistic random model. The algorithm has been designed with implementation simplicity in mind. Additionally, we describe how it can be adjusted to compute LCSk as well, which gives an improvement of the O(|X||̇Y |) algorithm presented in the original LCSk paper.