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Automaton-Based Sublinear Keyword Pattern Matching

机译:基于自动机的Sublinear关键字模式匹配

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摘要

We show how automaton-based sublinear keyword pattern matching (skpm) algorithms appearing in the literature can be seen as different instantiations of a general automaton-based skpm algorithm skeleton. Such algorithms use finite automata (FA) for efficient computation of string membership in a certain language. The algorithms were formally derived as part of a new skpm algorithm taxonomy, based on an earlier suffix-based skpm algorithm taxonomy. Such a taxonomy is based on deriving the algorithms from a common starting point by successively adding algorithm and problem details and has a number of advantages. It provides correctness arguments, clarifies the working of the algorithms and their interrelationships, helps in implementing the algorithms, and may lead to new algorithms being discovered by finding gaps in the taxonomy. We show how to arrive at the general algorithm skeleton and derive some instantiations, leading to well-known factor- and factor oracle-based algorithms. In doing so, we show the shift functions used for them can be (strengthenings of) shift functions used for suffix-based algorithms. This also results in a number of previously undescribed factor-based skpm algorithm variants, whose performance remains to be investigated.
机译:我们展示了在文献中出现的基于自动化的Sublinear关键字模式匹配(SKPM)算法可以看作是一种基于Automaton的SKPM算法骨架的不同实例。此类算法使用有限自动机(FA)以便以某种语言有效地计算字符串成员资格。基于早期的基于后缀的SKPM算法分类,该算法被正式导出为新的SKPM算法分类学的一部分。这种分类学基于通过连续添加算法和问题详细信息来从共同起点导出算法并具有许多优点。它提供了正确的参数,阐明了算法的工作及其相互关系,有助于实施算法,并可能导致通过在分类中找到差距来发现的新算法。我们展示了如何到达一般算法骨架并导致一些实例化,导致众所周知的因子和基于Oracle的算法。在这样做时,我们显示了用于它们的换档功能可以是基于后缀的算法使用的换档功能。这也导致许多以前未描述的基于因子的SKPM算法变体,其性能仍有待研究。

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