重复感知数据结构的实际组合
Practical combinations of repetition-aware data structures
摘要：
高度重复的字符串集合越来越多地被基因组测序和遗传变异实验所积累，以及存储人类生成的文件的所有版本，如网页和源代码。现有的索引用于在高度重复的字符串中定位模式的所有精确出现，它们利用了重复的单个度量。然而，在高度重复的字符串中，重复的多个不同度量都呈亚线性增长。在本文中，我们将探讨结合数据结构的实际优势，其大小取决于不同的重复措施。我们的结构的主要成分是游程长度编码的BWT（RLBWT），它需要的空间成比例的运行在一个字符串的Burrows-Wheeler变换的数量。我们描述了一系列实际的变体，结合RLBWT与字符串的Lempel-Ziv 77因子的边界集，其占用的空间与因子的数量成比例。这些变体分别使用字符串的RLBWT和其反向的RLBWT，或者在双向索引中只使用一个RLBWT，或者只使用一个支持单向提取的RLBWT。我们还研究了RLBWT与紧凑的有向非循环字图的字符串，一个数据结构，需要的空间成比例的最大重复的一个字符的扩展的数量相结合的实际优势。我们的方法易于实现，并在重要的数据集上提供竞争性权衡。
英文摘要：
Highly-repetitive collections of strings are increasingly being amassed by genome sequencing and genetic variation experiments, as well as by storing all versions of human-generated files, like webpages and source code. Existing indexes for locating all the exact occurrences of a pattern in a highly-repetitive string take advantage of a single measure of repetition. However, multiple, distinct measures of repetition all grow sublinearly in the length of a highly-repetitive string. In this paper we explore the practical advantages of combining data structures whose size depends on distinct measures of repetition. The main ingredient of our structures is the run-length encoded BWT (RLBWT), which takes space proportional to the number of runs in the Burrows-Wheeler transform of a string. We describe a range of practical variants that combine RLBWT with the set of boundaries of the Lempel-Ziv 77 factors of a string, which take space proportional to the number of factors. Such variants use, respectively, the RLBWT of a string and the RLBWT of its reverse, or just one RLBWT inside a bidirectional index, or just one RLBWT with support for unidirectional extraction. We also study the practical advantages of combining RLBWT with the compact directed acyclic word graph of a string, a data structure that takes space proportional to the number of one-character extensions of maximal repeats. Our approaches are easy to implement, and provide competitive tradeoffs on significant datasets.
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。
