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The Great Textual Hoax: Boosting Sampled String Matching with Fake Samples

Authors Simone Faro , Francesco Pio Marino, Andrea Moschetto, Arianna Pavone , Antonio Scardace

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Author Details

Simone Faro
  • Department of Mathematics and Computer Science, University of Catania, Italy
Francesco Pio Marino
  • Department of Mathematics and Computer Science, University of Catania, Italy
  • Univ Rouen Normandie, INSA Rouen Normandie, Université Le Havre Normandie, Normandie Univ, LITIS UR 4108, CNRS NormaSTIC FR 3638, IRIB, Rouen, F-76000, France
Andrea Moschetto
  • Department of Mathematics and Computer Science, University of Catania, Italy
Arianna Pavone
  • Department of Mathematics and Computer Science, University of Palermo, Italy
Antonio Scardace
  • Department of Mathematics and Computer Science, University of Catania, Italy

Funding

  • Faro, Simone: Supported by the National Centre for HPC, Big Data and Quantum Computing, Project CN00000013, affiliated to Spoke 10, co-founded by the European Union - NextGenerationEU.
  • Pavone, Arianna: Supported by PNRR project ITSERR - Italian Strengthening of the ESFRI RI RESILIENCE.

Cite AsGet BibTex

Simone Faro, Francesco Pio Marino, Andrea Moschetto, Arianna Pavone, and Antonio Scardace. The Great Textual Hoax: Boosting Sampled String Matching with Fake Samples. In 12th International Conference on Fun with Algorithms (FUN 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 291, pp. 13:1-13:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.FUN.2024.13

Abstract

Sampled String Matching is presented as an efficient solution to the string matching problem, aiming to tackle the space constraints of indexed string matching while purportedly reducing search times for online solutions. Despite the problem’s inception dating back to 1991, practical solutions have only recently emerged. These purportedly accelerate online searches by up to 35 times compared to conventional methods, achieved through a partial index occupying a mere 5% of the text size. This paper delves into the intricacies of one of the latest and most effective text sampling techniques, character distance sampling, which revolves around sampling distances between characters of a selected alphabet within the text. Specifically, we introduce fake samples while remaining honest! In other words, the study reveals that, interestingly, strategically introducing fake samples within the sampled sequence slashes the required index space by almost half, just avoid compromising the algorithm’s correctness. Additionally, since efficiency is everything, this approach, in turn, purportedly enhances the algorithm’s efficiency under specific conditions.

Subject Classification

ACM Subject Classification
  • Theory of computation → Pattern matching
  • Information systems → Information retrieval
Keywords
  • string matching
  • sampling

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