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Streaming Pattern Matching with d Wildcards

机译:流媒体模式与D通配符匹配

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In the pattern matching with d wildcards problem one is given a text T of length n and a pattern P of length m that contains d wildcard characters, each denoted by a special symbol '?'. A wildcard character matches any other character. The goal is to establish for each m-length substring of T whether it matches P. In the streaming model variant of the pattern matching with d wildcards problem the text T arrives one character at a time and the goal is to report, before the next character arrives, if the last m characters match P while using only o(m) words of space. In this paper we introduce two new algorithms for the d wildcard pattern matching problem in the streaming model. The first is a randomized Monte Carlo algorithm that is parameterized by a constant 0 = delta = 1. This algorithm uses (O) over tilde (d(1-delta)) amortized time per character and (O) over tilde (d(1+delta)) words of space. The second algorithm, which is used as a black box in the first algorithm, is a randomized Monte Carlo algorithm which uses O(d + log m) worst-case time per character and O(d + log m) words of space.
机译:在与D通配符匹配的模式中,问题一个是长度n的文本t和包含d通配符的长度m的模式p,每个字符由特殊符号“?”表示。通配符匹配任何其他字符。目标是建立T的每个M长度子字符串是否与P.与D通配符的模式匹配的流式模型变体中,文本T一次到达一个角色,目标是在下一个之前报告字符到达,如果最后一个字符匹配p仅使用空间的O(m)单词。在本文中,我们在流模型中介绍了D通配符模式匹配问题的两个新算法。首先是随机蒙特卡罗算法,其由常数0 <= delta <= 1.该算法使用(o)在数字上使用(d(1-delta))摊销/字符(d)上的摊销时间(d)over tilde(d (1 + delta))空间的话。第二算法用作第一算法中的黑盒子,是一个随机蒙特卡罗算法,它使用o(d + log m)每个字符的最坏情况时间和空间的O(d + log m)单词。

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