A wavelet forest for a text $T [1..n]$ over an alphabet $\sigma$ takes $n H_0 (T) + o (n \log \sigma)$ bits of space and supports access and rank on $T$ in $O (\log \sigma)$ time. K\"arkk\"ainen and Puglisi (2011) implicitly introduced wavelet forests and showed that when $T$ is the Burrows-Wheeler Transform (BWT) of a string $S$, then a wavelet forest for $T$ occupies space bounded in terms of higher-order empirical entropies of $S$ even when the forest is implemented with uncompressed bitvectors. In this paper we show experimentally that wavelet forests also have better access locality than wavelet trees and are thus interesting even when higher-order compression is not effective on $S$, or when $T$ is not a BWT at all.

翻译：对于字母表 $\sigma$ 上的文本 $T [1..n]$，小波森林占用 $n H_0 (T) + o (n \log \sigma)$ 比特的空间，并支持在 $O (\log \sigma)$ 时间内对 $T$ 进行访问和排名操作。K\"arkk\"ainen 和 Puglisi (2011) 隐含地引入了小波森林，并指出当 $T$ 是字符串 $S$ 的伯罗斯-惠勒变换（BWT）时，即使小波森林使用未压缩的位向量实现，其对 $T$ 所占用的空间也受限于 $S$ 的高阶经验熵。本文通过实验表明，小波森林在访问局部性方面优于小波树，因此即使在 $S$ 上高阶压缩无效，或 $T$ 根本不是 BWT 的情况下，小波森林也依然具有研究价值。