Wow! eBook
Probabilistic Data Structures and Algorithms for Big Data Applications
Author(s): Andrii Gakhov (Author)
- Publisher: Bod – Books on Demand
- Publication Date: August 5, 2022
- Language: English
- Print length: 220 pages
- ISBN-10: 3748190484
- ISBN-13: 9783748190486
Book Description
A technical book about popular space-efficient data structures and fast algorithms that are extremely useful in modern Big Data applications. 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.
Editorial Reviews
From the Author
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.Writing this book was an incredible experience! I hope it lets you a better understanding of Big Data applications and inspires to explore other problems and develop new amazing solutions.
About the Author
Andrii Gakhov is a mathematician and software engineer holding a Ph.D. in mathematical modeling and numerical methods. He has been a teacher in the School of Computer Science at V. Karazin Kharkiv National University in Ukraine for a number of years and currently works as a software practitioner for ferret go GmbH, the leading community moderation, automation, and analytics company in Germany. His fields of interests include machine learning, stream mining, and data analysis.
