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Wavelet Forests Revisited

Authors Eric Chiu , Dominik Kempa

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LIPIcs.SEA.2026.11.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Data compression
  • Information systems → Information retrieval
Keywords
  • wavelet tree
  • wavelet forest
  • select queries

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Abstract

Rank and select queries are basic operations on sequences, with applications in compressed text indexes and other space-efficient data structures. One of the standard data structures supporting these queries is the wavelet tree. In this paper, we study wavelet forests, that is, wavelet-tree structures based on the fixed-block compression boosting technique. Such structures partition the input sequence into fixed-size blocks and build a separate wavelet tree for each block. Previous work showed that this approach yields strong practical performance for rank queries.
We extend wavelet forests to support select queries. We show that select support can be added with little additional space overhead and that the resulting structures remain practically efficient. In experiments on a range of non-repetitive and repetitive inputs, wavelet forests are competitive with, and in most cases outperform, standalone wavelet-tree implementations. We also study the effect of internal parameters, including superblock size and navigational data, on select-query performance.

Cite As Get BibTex

Eric Chiu and Dominik Kempa. Wavelet Forests Revisited. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 11:1-11:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.SEA.2026.11

Author Details

Eric Chiu
  • Department of Computer Science, Stony Brook University, NY, USA
Dominik Kempa
  • Department of Computer Science, Stony Brook University, NY, USA

Funding

Partially funded by the NSF CAREER Award 2337891.

Supplementary Materials

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