Probabilistic Data Structures and Algorithms for Big Data Applications

Gakhov, Andrii

Probabilistic Data Structures and Algorithms for Big Data Applications

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商品描述

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.

商品描述(中文翻譯)

這本技術書籍介紹了在現代大數據應用中非常有用的流行節省空間的數據結構和快速算法。本書的目的是向技術從業人員，包括軟件架構師和開發人員，以及技術決策者介紹概率數據結構和算法。通過閱讀本書，您將獲得對概率數據結構的理論和實踐理解，並了解它們的常見用途。

作者簡介

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.

作者簡介(中文翻譯)

Andrii Gakhov是一位數學家和軟體工程師，擁有數學建模和數值方法的博士學位。他在烏克蘭的V. Karazin Kharkiv National University的計算機科學學院擔任教師多年，目前在德國領先的社群管理、自動化和分析公司ferret go GmbH擔任軟體實踐者。他的研究領域包括機器學習、流式挖掘和數據分析。

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