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Gakhov A. Probabilistic Data Structures and Algorithms for Big Data Applications

  • Файл формата djvu
  • размером 20,02 МБ
Gakhov A. Probabilistic Data Structures and Algorithms for Big Data Applications
Books on Demand GmbH, 2019. — 220 p.
A technical book about popular space-efficient data structures and fast algorithms that are extremely useful in modern Big Data applications.
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 and errors are fully compensated for by extremely low memory requirements, constant query time, and scaling, the three factors that become essential in Big Data applications. The purpose of this book is to introduce technology practitioners which includes software architects and developers, as well as technology decision makers to probabilistic data structures and algorithms.
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. This is not a book for scientists only, but to gain the most out of it you will need to have basic mathematical knowledge and an understanding of the general theory of data structures and algorithms. This book consists of six chapters, each preceded by an introduction and followed by a brief summary and bibliography for further reading relating to that chapter. Every chapter is dedicated to one particular problem in Big Data applications, it starts with an in-depth explanation of the problem and follows by introducing data structures and algorithms that can be used to solve it efficiently.
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