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FOCS2021顶会

Small-space and streaming pattern matching with kk edits

Tomasz Kociumaka, Ely Porat, Tatiana Starikovskaya

2021年份
13被引次数
10顶会引用

摘要

In this work, we revisit the fundamental and well-studied problem of approximate pattern matching under edit distance. Given an integerkk, a patternPPof lengthmm, and a textTTof lengthn≥mn\geq m, the task is to find substrings ofTTthat are within edit distancekkfromPP. Our main result is a streaming algorithm that solves the problem inO~(k5)\tilde{\mathcal{O}}(k^{5})space11Hereafter,O~(⋅)\tilde{\mathcal{O}}(\cdot)hides apoly(log⁡n)\text{poly} (\log n)factor. andO~(k8)\tilde{\mathcal{O}}(k^{8})amortized time per character of the text, providing answers correct with high probability. This answers a decade-old question: since the discovery of a poly (k log nk\ \text{log}\ n) -space streaming algorithm for pattern matching under Hamming distance by Porat and Porat [FOCS 2009], the existence of an analogous result for edit distance remained open. Up to this work, no poly (k log nk\ \text{log}\ n)-space algorithm was known even in the simpler semi-streaming model, whereTTcomes as a stream butPPis available for read-only access. In this model, we give a deterministic algorithm that achieves slightly better complexity. Our central technical contribution is a new space-efficient deterministic encoding of two strings, called the greedy encoding, which encodes a set of all alignments of cost at mostkkwith a certain property (we call such alignments greedy). On strings of length at mostnn, the encoding occupiesO~(k2)\tilde{\mathcal{O}}(k^{2})space. We use the encoding to compress substrings of the text that are close to the pattern. In order to do so, we compute the encoding for substrings of the text and of the pattern, which requires read-only access to the latter. In order to develop the fully streaming algorithm, we further introduce a new edit distance sketch parameterized by integersn>kn > k. For any string of length at mostnn, the sketch is of sizeO~(k‾2)\tilde{\mathcal{O}}\overline{(k}^{2}), and it can be computed with anO~(k2)\tilde{\mathcal{O}}(k^{2})-space streaming algorithm. Given the sketches of two strings, inO~(k3)\tilde{\mathcal{O}}(k^{3})time we can compute their edit distance or certify that it is larger thankk. This result improves uponO~(k8)\tilde{\mathcal{O}}(k^{8})-size sketches of Belazzougui and Zhang [FOCS 2016] and very recentO~(k3)\tilde{\mathcal{O}}(k^{3})-size sketches of Jin, Nelson, and Wu [STACS 2021].

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