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Faster Block Tree Construction

Authors Dominik Köppl , Florian Kurpicz , Daniel Meyer

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Author Details

Dominik Köppl
  • Department of Computer Science, University of Muenster, Germany
Florian Kurpicz
  • Karlsruhe Institute of Technology, Germany
Daniel Meyer
  • Karlsruhe Institute of Technology, Germany

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 882500).
  • Köppl, Dominik: Supported by JSPS KAKENHI Grant Numbers JP21K17701 and JP23H04378.

Cite AsGet BibTex

Dominik Köppl, Florian Kurpicz, and Daniel Meyer. Faster Block Tree Construction. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 74:1-74:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ESA.2023.74

Abstract

The block tree [Belazzougui et al. J. Comput. Syst. Sci. '21] is a compressed text index that can answer access (extract a character at a position), rank (number of occurrences of a specified character in a prefix of the text), and select (size of smallest prefix such that a specified character has a specified rank) queries. It requires O(zlog(n/z)) words of space, where z is the number of Lempel-Ziv factors of the text. For some highly repetitive inputs, a block tree can require as little as 0.015 bits per character of the text. Small values of z make the block tree a space-efficient alternative to the wavelet tree, which is another index for these three types of queries. While wavelet trees can be constructed fast in practice, up so far compressed versions of the wavelet tree only leverage statistical compression, meaning that they are blind to spaced repetitions. To make block trees usable in practice, a first step is to find ways in constructing them efficiently. We address this problem by presenting a practically efficient construction algorithm for block trees, which is up to an order of magnitude faster than previous implementations. Additionally, we parallelize our implementation, making it the first block tree construction implementation that works in parallel in shared memory.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data compression
  • Theory of computation → Pattern matching
Keywords
  • compressed data structure
  • block tree
  • Lempel-Ziv compression
  • longest previous factor array
  • rank and select

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References

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