Pearls of Algorithm Engineering

This book provides a unique approach to designing and analyzing algorithms for big data applications, discussing core and classic problems and presenting elegant solutions with pseudocode and running examples.

Format: Hardback
Length: 326 pages
Publication date: 22 June 2023
Publisher: Cambridge University Press

Numerous textbooks delve into the realm of algorithms, emphasizing big-O notation and fundamental design principles. However, this book stands out by offering a distinctive approach to elevating design and analysis to a realm of predictable practical efficiency. It delves into core and classic algorithmic problems that emerge in the development of big data applications, presenting elegant solutions of increasing sophistication and efficiency. Solutions are analyzed within both the classic RAM model and the more practically significant external-memory model, enabling the evaluation of I/O complexity. Chapters encompass a wide range of data types, including integers, strings, trees, and graphs, along with algorithmic tools such as sampling, sorting, data compression, and searching in dictionaries and texts. Additionally, the book explores recent developments regarding compressed data structures. Algorithmic solutions are accompanied by detailed pseudocode and numerous running examples, enriching the toolboxes of students, researchers, and professionals seeking effective and efficient processing of big data.

Numerous textbooks delve into the realm of algorithms, emphasizing big-O notation and fundamental design principles. However, this book stands out by offering a distinctive approach to elevating design and analysis to a realm of predictable practical efficiency. It delves into core and classic algorithmic problems that emerge in the development of big data applications, presenting elegant solutions of increasing sophistication and efficiency. Solutions are analyzed within both the classic RAM model and the more practically significant external-memory model, enabling the evaluation of I/O complexity. Chapters encompass a wide range of data types, including integers, strings, trees, and graphs, along with algorithmic tools such as sampling, sorting, data compression, and searching in dictionaries and texts. Additionally, the book explores recent developments regarding compressed data structures. Algorithmic solutions are accompanied by detailed pseudocode and numerous running examples, enriching the toolboxes of students, researchers, and professionals seeking effective and efficient processing of big data.

ISBN-13: 9781009123280