Having this book close at hand is incredibly convenient. It is the first book to systematically present the findings of research on algorithmic difficulties, both theoretical and practical, in texts up to the most current advancements in stringology.

This much-needed work stresses both theoretical underpinnings and real-world applications while discussing the construction of algorithms and data structures for text processing. It is meant to serve as a reference for computer science experts as well as a textbook for courses on algorithm creation, particularly those connected to text processing. The book adopts a distinctive strategy and delves further into its subject than other more general works. It includes both traditional algorithms and current findings from related research.

The book is the first text to compile a variety of text algorithms, many of which are brand-new and making their debut here. Although they are well-known, other algorithms have never been written up in a publication. Karp, Miller, and Rosenberg's algorithm and Weiner's algorithm are two examples of significant algorithms. They are shown together for the first time in this place.

The content on suffix trees and subword graphs, their uses, fresh methods for time-space optimal string matching, and text compression make up the bulk of the book. Basic parallel algorithms for text issues are also taught. Applications of each of these algorithms are provided for issues with text processing tools, natural language processing, data compression software, and data retrieval systems.

From a theoretical standpoint, the book provides a veritable wealth of paradigms for the creation of effective algorithms, offering the necessary framework for developing useful software that deals with sequences. The creation of a methodology for presenting text algorithms in a way that allows for complete comprehension is a key component of the authors' approach.

The book emphasizes algorithm efficiency throughout, contending that it is essential to their utility. This is crucial because the algorithms discussed here will be employed in "Big Science" fields like molecular sequence analysis, where the current generation of software has struggled to keep up with the exponential rise of data.

Finally, the book can be seen as a mathematical basis for the study and creation of text processing algorithms due to the development of its theoretical framework.