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Bidirectional String Anchors: A New String Sampling Mechanism

Authors Grigorios Loukides , Solon P. Pissis

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Author Details

Grigorios Loukides
  • Department of Informatics, King’s College London, UK
Solon P. Pissis
  • CWI, Amsterdam, The Netherlands
  • Vrije Universiteit, Amsterdam, The Netherlands

Funding

  • Loukides, Grigorios: This paper is part of the Leverhulme Trust RPG-2019-399 project.
  • Pissis, Solon P.: This paper is part of the PANGAIA project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 872539. This paper is also part of the ALPACA project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956229.

Acknowledgements

We would like to thank Tomasz Kociumaka for pointing us to [Tomasz Kociumaka, 2016] and Michelle Sweering for useful discussions.

Cite AsGet BibTex

Grigorios Loukides and Solon P. Pissis. Bidirectional String Anchors: A New String Sampling Mechanism. In 29th Annual European Symposium on Algorithms (ESA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 204, pp. 64:1-64:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.ESA.2021.64

Abstract

The minimizers sampling mechanism is a popular mechanism for string sampling introduced independently by Schleimer et al. [SIGMOD 2003] and by Roberts et al. [Bioinf. 2004]. Given two positive integers w and k, it selects the lexicographically smallest length-k substring in every fragment of w consecutive length-k substrings (in every sliding window of length w+k-1). Minimizers samples are approximately uniform, locally consistent, and computable in linear time. Although they do not have good worst-case guarantees on their size, they are often small in practice. They thus have been successfully employed in several string processing applications. Two main disadvantages of minimizers sampling mechanisms are: first, they also do not have good guarantees on the expected size of their samples for every combination of w and k; and, second, indexes that are constructed over their samples do not have good worst-case guarantees for on-line pattern searches. To alleviate these disadvantages, we introduce bidirectional string anchors (bd-anchors), a new string sampling mechanism. Given a positive integer 𝓁, our mechanism selects the lexicographically smallest rotation in every length-𝓁 fragment (in every sliding window of length 𝓁). We show that bd-anchors samples are also approximately uniform, locally consistent, and computable in linear time. In addition, our experiments using several datasets demonstrate that the bd-anchors sample sizes decrease proportionally to 𝓁; and that these sizes are competitive to or smaller than the minimizers sample sizes using the analogous sampling parameters. We provide theoretical justification for these results by analyzing the expected size of bd-anchors samples. We also show that by using any bd-anchors sample, we can construct, in near-linear time, an index which requires linear (extra) space in the size of the sample and answers on-line pattern searches in near-optimal time. We further show, using several datasets, that a simple implementation of our index is consistently faster for on-line pattern searches than an analogous implementation of a minimizers-based index [Grabowski and Raniszewski, Softw. Pract. Exp. 2017]. Finally, we highlight the applicability of bd-anchors by developing an efficient and effective heuristic for top-K similarity search under edit distance. We show, using synthetic datasets, that our heuristic is more accurate and more than one order of magnitude faster in top-K similarity searches than the state-of-the-art tool for the same purpose [Zhang and Zhang, KDD 2020].

Subject Classification

ACM Subject Classification
  • Theory of computation → Pattern matching
Keywords
  • string algorithms
  • string sampling
  • text indexing
  • top-K similarity search

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