Asymmetric Streaming Approximate Pattern Matching

Abstract

We study the space complexity of pattern matching in the asymmetric streaming model, focusing on approximate pattern matching under the Hamming and edit distances. In this problem, we are given an m-length pattern and an n-length text and must compute, for every position of the text, the smallest distance between the pattern and a substring of the text which ends at this position. In the asymmetric streaming model, we assume to have constant-time random access to the pattern, while the text arrives as a stream, one letter at a time. 
It is known that computing all distances exactly in the asymmetric streaming model requires Ω(m) space (for the edit distance see Li and Zheng [FSTTCS 2021]). Hence, to achieve sublinear space, a relaxation of the problem is necessary. One possible variant is to consider the small distance regime, where the algorithm must compute only those distances that are bounded by a small integer parameter k. In this case, existing algorithms in a more restrictive fully streaming model (Kociumaka, Clifford, Porat [SODA'19], Bhattacharya, Koucký [ICALP'23]) straightforwardly imply the existence of poly(k, log n)-space asymmetric streaming algorithms. Another possible relaxation is computing all distances approximately. For this variant, we don't have small-space algorithms in the fully streaming model: the best known algorithm solves pattern matching under the Hamming distance (1+ε)-approximately using 𝒪̃(ε^{-2}√m) space (Starikovskaya, Svagerka, Uznański [APPROX'20]). For the edit distance, no efficient approximation algorithms are known.
In this work, we show approximation algorithms for pattern matching under the Hamming and edit distances in the asymmetric streaming model for any constant ε > 0:  
1) We show that there is a simple randomised asymmetric streaming algorithm that solves approximate pattern matching under the Hamming distance (1+ε)-approximately using 𝒪(ε^{-3}log³n) bits. 
2) As our second and main contribution, we extend the result of Cheng et al. [ICALP 2021] and show that for any integer k there is a deterministic asymmetric streaming algorithm that solves pattern matching under the edit distance (2^k-1+ε)-approximately using 𝒪̃(m^{1/k}) space.