Let string $S[1..n]$ be parsed into z phrases by the Lempel-Ziv algorithm. The corresponding compression algorithm encodes S in $\mathcal{O}\left(z\right)$ space, but it does not support random access to S. We introduce a data structure, the block tree, that represents S in $\mathcal{O}(z\log (n/z))$ space and extracts any symbol of S in time $\mathcal{O}(\log (n/z))$, among other space-time tradeoffs. The structure also supports other queries that are useful for building compressed data structures on top of S. Further, block trees can be built in linear time and in a scalable manner. Our experiments show that block trees offer relevant space-time tradeoffs compared to other compressed string representations for highly repetitive strings.

让字符串 $\mathrm{小号}[\mathrm{1个}。。ñ]$被Lempel-Ziv算法解析为z个短语。相应的压缩算法将S编码为$\mathcal{Ø}（ž）$空间，但它不支持随机访问小号。我们介绍了一个数据结构，即块树，它表示S中的$\mathcal{Ø}（ž\mathrm{日志}（ñ/ž））$空间并及时提取S的任何符号$\mathcal{Ø}（\mathrm{日志}（ñ/ž））$，以及其他时空权衡。该结构还支持其他查询，这些查询对于在S上构建压缩数据结构很有用。此外，可以在线性时间内以可伸缩的方式构建块树。我们的实验表明，对于高度重复的字符串，与其他压缩字符串表示形式相比，块树提供了相关的时空折衷。