相似文本的在线模式匹配

R. Grossi, C. Iliopoulos, Chang Liu, N. Pisanti, S. Pissis, Ahmad Retha, Giovanna Rosone, Fatima Vayani, Luca Versari
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引用次数: 23

摘要

一组相似文本的模式匹配受到了广泛的关注,特别是最近,主要是由于它在人类遗传变异编目中的应用。特别是,对于这个问题的离线版本,已经提出了许多不同的算法;也就是说,为一组相似的文本构造一个压缩索引,以便有效地回答模式匹配查询。然而,这个问题的更基本的在线版本是一个相当不发达的话题。解决方案的在线版本可能是有益的,原因有很多;例如,有效的在线解决方案可以与部分索引结合使用,作为实际的权衡。在这里,我们试图通过提出两种有效的算法来解决这个问题。值得注意的是,对于短模式,其中一种算法要求文本表示的大小呈时间线性。此外,实验结果在实践中证实了我们的理论发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On-Line Pattern Matching on Similar Texts
Pattern matching on a set of similar texts has received much attention, especially recently, mainly due to its application in cataloguing human genetic variation. In particular, many different algorithms have been proposed for the off-line version of this problem; that is, constructing a compressed index for a set of similar texts in order to answer pattern matching queries efficiently. However, the on-line, more fundamental, version of this problem is a rather undeveloped topic. Solutions to the on-line version can be beneficial for a number of reasons; for instance, efficient on-line solutions can be used in combination with partial indexes as practical trade-offs. We make here an attempt to close this gap via proposing two efficient algorithms for this problem. Notably, one of the algorithms requires time linear in the size of the texts' representation, for short patterns. Furthermore, experimental results confirm our theoretical findings in practical terms.
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