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

Navarro, Gonzalo, Raffinot, Mathieu

ISBN 10: 0521813077 / ISBN 13: 9780521813075

Published by Cambridge University Press, 2002

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Seller Inventory # D8S0-3-M-0521813077-4

Bibliographic Details

• Title: Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences
• Publisher: Cambridge University Press
• Publication Date: 2002
• Language: English
• ISBN 10: 0521813077
• ISBN 13: 9780521813075
• Binding: Hardcover
• Condition: Good
• Book Type: book

About this title

Synopsis

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.

Book Description

Recent years have witnessed a dramatic increase of interest in sophisticated string matching problems, mainly arising from information retrieval and computational biology. In this book, the authors present several new string matching algorithms, developed to handle these complex new problems. Unlike most other books, the emphasis is those techniques that work best in practice, which are also the simplest ones in general. 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.