Search Results

Documents authored by Ferragina, Paolo

Document
DOI: 10.4230/LIPIcs.CPM.2025.7
FL-RMQ: A Learned Approach to Range Minimum Queries

Authors: Paolo Ferragina and Filippo Lari

Published in: LIPIcs, Volume 331, 36th Annual Symposium on Combinatorial Pattern Matching (CPM 2025)

Abstract
We address the problem of designing and implementing a data structure for the Range Minimum Query problem. We show a surprising connection between this classical problem and the geometry of a properly defined set of points in the Cartesian plane. Building on this insight, we hinge upon a well-known result in Computational Geometry to introduce the first RMQ solution that exploits (i.e., learns) the distribution of such 2D-points via proper error-bounded linear approximations. Because of these features, we name the resulting data structure: Fully-Learned RMQ, shortly FL-RMQ. We prove theoretical bounds for its space usage and query time, covering both worst-case scenarios and average-case performance for uniformly distributed inputs. These bounds compare favorably with the ones achievable by the best-known indexing solutions (i.e., the ones that allow access to the indexed array), especially when the input data follow some geometric regularities that we characterize in the paper, thus providing principled evidence of FL-RMQ being a novel data-aware solution to the RMQ problem. We corroborate our theoretical findings with a wide set of experiments showing that FL-RMQ offers more robust space-time trade-offs than the other known practical indexing solutions on both artificial and real-world datasets. We believe that our novel approach to the RMQ problem is noteworthy not only for its interesting space-time trade-offs, but also because it is flexible enough to be applied easily to the encoding variant of RMQ (i.e., the one that does not allow access to the indexed array), and moreover, because it paves the way to research opportunities on possibly other problems.
Cite as

Paolo Ferragina and Filippo Lari. FL-RMQ: A Learned Approach to Range Minimum Queries. In 36th Annual Symposium on Combinatorial Pattern Matching (CPM 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 331, pp. 7:1-7:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{ferragina_et_al:LIPIcs.CPM.2025.7,
  author =	{Ferragina, Paolo and Lari, Filippo},
  title =	{{FL-RMQ: A Learned Approach to Range Minimum Queries}},
  booktitle =	{36th Annual Symposium on Combinatorial Pattern Matching (CPM 2025)},
  pages =	{7:1--7:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-369-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{331},
  editor =	{Bonizzoni, Paola and M\"{a}kinen, Veli},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2025.7},
  URN =		{urn:nbn:de:0030-drops-231014},
  doi =		{10.4230/LIPIcs.CPM.2025.7},
  annote =	{Keywords: Range-Minimum query, Learned data structures, Compact data structures, Experimental results}
}
Document
DOI: 10.4230/LIPIcs.ESA.2023.46
Learned Monotone Minimal Perfect Hashing

Authors: Paolo Ferragina, Hans-Peter Lehmann, Peter Sanders, and Giorgio Vinciguerra

Published in: LIPIcs, Volume 274, 31st Annual European Symposium on Algorithms (ESA 2023)

Abstract
A Monotone Minimal Perfect Hash Function (MMPHF) constructed on a set S of keys is a function that maps each key in S to its rank. On keys not in S, the function returns an arbitrary value. Applications range from databases, search engines, data encryption, to pattern-matching algorithms. In this paper, we describe LeMonHash, a new technique for constructing MMPHFs for integers. The core idea of LeMonHash is surprisingly simple and effective: we learn a monotone mapping from keys to their rank via an error-bounded piecewise linear model (the PGM-index), and then we solve the collisions that might arise among keys mapping to the same rank estimate by associating small integers with them in a retrieval data structure (BuRR). On synthetic random datasets, LeMonHash needs 34% less space than the next larger competitor, while achieving about 16 times faster queries. On real-world datasets, the space usage is very close to or much better than the best competitors, while achieving up to 19 times faster queries than the next larger competitor. As far as the construction of LeMonHash is concerned, we get an improvement by a factor of up to 2, compared to the competitor with the next best space usage. We also investigate the case of keys being variable-length strings, introducing the so-called LeMonHash-VL: it needs space within 13% of the best competitors while achieving up to 3 times faster queries than the next larger competitor.
Cite as

Paolo Ferragina, Hans-Peter Lehmann, Peter Sanders, and Giorgio Vinciguerra. Learned Monotone Minimal Perfect Hashing. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 46:1-46:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{ferragina_et_al:LIPIcs.ESA.2023.46,
  author =	{Ferragina, Paolo and Lehmann, Hans-Peter and Sanders, Peter and Vinciguerra, Giorgio},
  title =	{{Learned Monotone Minimal Perfect Hashing}},
  booktitle =	{31st Annual European Symposium on Algorithms (ESA 2023)},
  pages =	{46:1--46:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-295-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{274},
  editor =	{G{\o}rtz, Inge Li and Farach-Colton, Martin and Puglisi, Simon J. and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2023.46},
  URN =		{urn:nbn:de:0030-drops-186990},
  doi =		{10.4230/LIPIcs.ESA.2023.46},
  annote =	{Keywords: compressed data structure, monotone minimal perfect hashing, retrieval}
}
Document
DOI: 10.4230/LIPIcs.ISAAC.2021.64
Repetition- and Linearity-Aware Rank/Select Dictionaries

Authors: Paolo Ferragina, Giovanni Manzini, and Giorgio Vinciguerra

Published in: LIPIcs, Volume 212, 32nd International Symposium on Algorithms and Computation (ISAAC 2021)

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.
Cite as

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)

Copy BibTex To Clipboard

@InProceedings{ferragina_et_al:LIPIcs.ISAAC.2021.64,
  author =	{Ferragina, Paolo and Manzini, Giovanni and Vinciguerra, Giorgio},
  title =	{{Repetition- and Linearity-Aware Rank/Select Dictionaries}},
  booktitle =	{32nd International Symposium on Algorithms and Computation (ISAAC 2021)},
  pages =	{64:1--64:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-214-3},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{212},
  editor =	{Ahn, Hee-Kap and Sadakane, Kunihiko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2021.64},
  URN =		{urn:nbn:de:0030-drops-154974},
  doi =		{10.4230/LIPIcs.ISAAC.2021.64},
  annote =	{Keywords: Data compression, Compressed data structures, Entropy}
}
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact