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arXiv 2605.00566cs.DS
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通过多层哈希进行参数化匹配

Set Parameterized Matching via Multi-Layer Hashing

Bar-Ilan University(巴伊兰大学)

Moshe Lewenstein, Ely Porat

AI总结:

本文提出一种随机算法,解决集合参数化匹配问题,通过多层哈希方案高效处理集合字符串匹配与动态编码挑战。

摘要:

我们研究了"集合参数化匹配"问题,这是Baker提出的经典参数化匹配问题的推广。在集合参数化匹配中,模式和文本都是由集合构成的序列,每个位置包含多个字符而非单个字符。如果存在一个双射映射将一个集合字符串映射到另一个集合字符串,则称为参数化匹配。Boussidan针对等长集合字符串的情况引入了该问题。我们提出了一种随机算法,运行时间为O(N + M)(高概率),其中N是文本大小,M是模式大小。我们的方法采用了一种基于Karp-Rabin指纹的新型三重哈希方案,以解决以下挑战:(1) 问题表示的大小爆炸,(2) 集合到集合的匹配,以及(3) 模式扫描过程中文本子串编码的动态性质。

英文摘要:

We study the "set parameterized matching" problem, a generalization of the classical parameterized matching problem introduced by Baker. In set parameterized matching, both the pattern and text are sequences where each position contains a set of characters rather than a single character. Two set-strings parameterized match if there exists a bijection between their alphabets that maps one to the other set-wise. Boussidan introduced this problem for the case of equal-length set-strings. We present a randomized algorithm running in $O(N + M)$ time with high probability, where $N$ is the text size and $M$ is the pattern size. Our approach employs a novel three-layer hashing scheme based on Karp-Rabin fingerprinting that addresses the challenges of (1) the size blowup in representations of the problem, (2) set-to-set matching, and (3) the dynamic nature of encodings of text substrings during pattern scanning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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