在构建压缩后数组和后树的过程中打破了-障碍
Dominik Kempa1, Tomasz Kociumaka2
1Stony Brook University.
Proceedings of the ... Annual ACM-SIAM Symposium on Discrete Algorithms. ACM-SIAM Symposium on Discrete Algorithms
|June 5, 2024
概括
这项研究引入了新的压缩后数组和树,具有更快的构建时间,改进了用于字符串处理的现有数据结构. 这些进步为生物信息学和数据压缩应用提供了显著的加速.
科学领域:
- 字符串数据结构是一个字符串数据结构.
- 计算复杂性 计算复杂性
- 生物信息学算法的算法
背景情况:
- 后数组和后树是字符串处理的基础,但需要大量的空间.
- 压缩后数组 (CSA) 和FM索引提供空间效率,但施工时间较慢.
- 两个十年来,这些结构的建造时间一直是瓶.
研究的目的:
- 开发新的压缩后数组和压缩后树结构.
- 为了实现这些空间效率高的数据结构的更快的施工时间.
- 维护或改进现有的空间和查询时间复杂性.
主要方法:
- 提出了新的压缩后数组和压缩后树结构.
- 开发了具有改进建筑时间复杂性的算法.
- 减少了优化CSA/CST参数以预先排名和预先选择查询的问题.
主要成果:
- 对压缩后数组和树的构建时间达到了0n,这是一个显著的改进.
- 新的结构与现有的空间界限 (例如,n log 存储位) 和查询时间 (例如,每次操作的O ((log 存储位) 或O ((log 存储位)) 相匹配.
- 从CSA/CST参数向前等级/选择进行了总体的减少,从而实现了新的模式匹配索引.
结论:
- 开发的压缩后数组和树结构为20年来首次大幅改善建筑时间.
- 这些新结构为生物信息学和数据压缩中的空间关键应用提供了实际解决方案.
- 将前降低到排名/选择,为进一步研究高效的字符串索引开辟了道路.
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