Probabilistic Data Structures and Algorithms for Big Data Applications из жанра Научные

Probabilistic Data Structures and Algorithms for Big Data Applications

Другая компьютерная литература, Математика

Автор: Gakhov Andrii

Год: 2019

Добавил: Admin 17 Янв 24

Проверил: Admin 17 Янв 24

Формат: PDF (3090 Kb)

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Аннотация

Probabilistic data structures is a common name for data structures based mostly on different hashing techniques. Unlike regular (or deterministic) data structures, they always provide approximated answers but with reliable ways to estimate possible errors. Fortunately, the potential losses or errors are fully compensated for by extremely low memory requirements, constant query time, and scaling, three factors that become important in Big Data applications.

While it is impossible to cover all the existing amazing solutions, this book is to highlight their common ideas and important areas of application, including membership querying, counting, stream mining, and similarity estimation.

The purpose of this book is to introduce technology practitioners, including software architects and developers, as well as technology decision makers to probabilistic data structures and algorithms. Reading this book, you will get a theoretical and practical understanding of probabilistic data structures and learn about their common uses.

ISBN (paperback): 978-37-48190-48-6

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