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Cited by 291

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences

Navarro, Gonzalo, Raffinot, Mathieu·2002·Cambridge University Press eBooks

Abstract

String matching problems range from the relatively simple task of searching a single text for a string of characters to searching a database for approximate occurrences of a complex pattern. Recent years have witnessed a dramatic increase of interest in sophisticated string matching problems, especially in information retrieval and computational biology. This book presents a practical approach to string matching problems, focusing on the algorithms and implementations that perform best in practice. It covers searching for simple, multiple and extended strings, as well as regular expressions, and exact and approximate searching. It includes all the most significant new developments in complex pattern searching. The clear explanations, step-by-step examples, algorithm pseudocode, and implementation efficiency maps will enable researchers, professionals and students in bioinformatics, computer science, and software engineering to choose the most appropriate algorithms for their applications.

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Metadata

Authors
Navarro, G., & Raffinot, M.
Venue
Cambridge University Press eBooks
Published
2002-05-27
OpenAlex
W4210951666
Cited by
291
References
51
FWCI
2.72
Type
book
Language
en
Open access
closed

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CoChat. (2026). Citation check: Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences. https://app.cochat.ai/papers/10.1017/cbo9781316135228
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Navarro, G., & Raffinot, M. (2002). Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9781316135228

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.