Approximate pattern matching with k-mismatches in packed text

TL;DR

This paper introduces new algorithms for approximate pattern matching with k-mismatches in packed text, improving efficiency in specific computational models and extending techniques to other related problems.

Contribution

The authors develop faster algorithms for k-mismatch pattern matching in packed text, optimizing performance in the $AC^0$ and word-RAM models, and introduce techniques applicable to other approximate matching problems.

Findings

Improved time complexity bounds for $k$-mismatch pattern matching in packed text.

Algorithms outperform existing bounds for $w = ext{Omega}( ext{log}^{1+ ext{epsilon}} n)$.

Extended techniques to solve other approximate matching problems.

Abstract

Given strings $P$ of length $m$ and $T$ of length $n$ over an alphabet of size $\sigma$, the string matching with $k$-mismatches problem is to find the positions of all the substrings in $T$ that are at Hamming distance at most $k$ from $P$. If $T$ can be read only one character at the time the best known bounds are $O(n\sqrt{k\log k})$ and $O(n + n\sqrt{k/w}\log k)$ in the word-RAM model with word length $w$. In the RAM models (including $AC^0$ and word-RAM) it is possible to read up to $\floor{w / \log \sigma}$ characters in constant time if the characters of $T$ are encoded using $\ceil{\log \sigma}$ bits. The only solution for $k$-mismatches in packed text works in $O((n \log\sigma/\log n)\ceil{m \log (k + \log n / \log\sigma) / w} + n^{\varepsilon})$ time, for any $\varepsilon > 0$. We present an algorithm that runs in time $O(\frac{n}{\floor{w/(m\log\sigma)}} (1 + \log \min(k,\sigma) \log m / \log\sigma))$ in the $AC^0$ model if $m=O(w / \log\sigma)$ and $T$ is given packed. We also describe a simpler variant that runs in time $O(\frac{n}{\floor{w/(m\log\sigma)}}\log \min(m, \log w / \log\sigma))$ in the word-RAM model. The algorithms improve the existing bound for $w = \Omega(\log^{1+\epsilon}n)$, for any $\epsilon > 0$. Based on the introduced technique, we present algorithms for several other approximate matching problems.