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Repetition- and Linearity-Aware Rank/Select Dictionaries

Authors Paolo Ferragina , Giovanni Manzini , Giorgio Vinciguerra

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Author Details

Paolo Ferragina
  • Department of Computer Science, University of Pisa, Italy
Giovanni Manzini
  • Department of Computer Science, University of Pisa, Italy
Giorgio Vinciguerra
  • Department of Computer Science, University of Pisa, Italy

Funding

The authors have been supported in part by the Italian MUR PRIN project 2017WR7SHH: Multicriteria data structures and algorithms.

Cite AsGet BibTex

Paolo Ferragina, Giovanni Manzini, and Giorgio Vinciguerra. Repetition- and Linearity-Aware Rank/Select Dictionaries. In 32nd International Symposium on Algorithms and Computation (ISAAC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 212, pp. 64:1-64:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.ISAAC.2021.64

Abstract

We revisit the fundamental problem of compressing an integer dictionary that supports efficient rank and select operations by exploiting two kinds of regularities arising in real data: repetitiveness and approximate linearity. Our first contribution is a Lempel-Ziv parsing properly enriched to also capture approximate linearity in the data and still be compressed to the kth order entropy. Our second contribution is a variant of the block tree structure whose space complexity takes advantage of both repetitiveness and approximate linearity, and results highly competitive in time too. Our third and final contribution is an implementation and experimentation of this last data structure, which achieves new space-time trade-offs compared to known data structures that exploit only one of the two regularities.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data compression
  • Theory of computation → Data structures design and analysis
Keywords
  • Data compression
  • Compressed data structures
  • Entropy

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Supplementary Materials

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