# $L_p$ Pattern Matching in a Stream

## TL;DR

This paper develops new streaming algorithms for approximate pattern matching under various $L_p$ distances, significantly improving space efficiency for large-scale, noisy data such as biological sequences.

## Contribution

It introduces a suite of streaming algorithms for $L_p$ pattern matching with improved space complexity, extending previous work to broader $L_p$ norms and approximation guarantees.

## Key findings

- Achieved $ ilde{O}(rac{1}{ ext{ extsterling}^2}\sqrt{n})$ space algorithms for $L_p$ distances with $0 < p \,\leq 1$.
- Extended streaming pattern matching algorithms to $L_1$, $L_2$, and general $L_p$ norms.
- Significantly improved space efficiency over previous algorithms for large-scale, noisy data.

## Abstract

We consider the problem of computing distance between a pattern of length $n$ and all $n$-length subwords of a text in the streaming model. In the streaming setting, only the Hamming distance ($L_0$) has been studied. It is known that computing the exact Hamming distance between a pattern and a streaming text requires $\Omega(n)$ space (folklore). Therefore, to develop sublinear-space solutions, one must relax their requirements. One possibility to do so is to compute only the distances bounded by a threshold $k$, see~[SODA'19, Clifford, Kociumaka, Porat] and references therein. The motivation for this variant of this problem is that we are interested in subwords of the text that are similar to the pattern, i.e. in subwords such that the distance between them and the pattern is relatively small. On the other hand, the main application of the streaming setting is processing large-scale data, such as biological data. Recent advances in hardware technology allow generating such data at a very high speed, but unfortunately, the produced data may contain about 10\% of noise~[Biol. Direct.'07, Klebanov and Yakovlev]. To analyse such data, it is not sufficient to consider small distances only. A possible workaround for this issue is the $(1\pm\varepsilon)$-approximation. This line of research was initiated in [ICALP'16, Clifford and Starikovskaya] who gave a $(1\pm\varepsilon)$-approximation algorithm with space~$\tilde{O}(\varepsilon^{-5}\sqrt{n})$. In this work, we show a suite of new streaming algorithms for computing the Hamming, $L_1$, $L_2$ and general $L_p$ ($0 < p < 2$) distances between the pattern and the text. Our results significantly extend over the previous result in this setting. In particular, for the Hamming distance and for the $L_p$ distance when $0 < p \le 1$ we show a streaming algorithm that uses $\tilde{O}(\varepsilon^{-2}\sqrt{n})$ space for polynomial-size alphabets.