Вероятностные структуры данных и алгоритмы в приложениях Big Data

Книга раскрывает идеи и области применения вероятностных структур данных, включая запросы о принадлежности, подсчёт количества элементов и их частоты, анализ потоков и оценку сходства.

Изучайте теоретические аспекты вероятностных структур данных
Решайте практические задачи обработки больших данных
Находите подходящие структуры данных для ваших задач

Андрей Гахов

Изучайте теоретические аспекты вероятностных структур данных
Решайте практические задачи обработки больших данных
Находите подходящие структуры данных для ваших задач

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About the Book

Вероятностные структуры данных - это общее название для структур данных, основанных в основном на различных техниках хеширования. В отличие от обычных (или детерминированных) структур данных, они дают приближённые ответы, но с надёжными способами оценки возможных неточностей. К счастью, потенциальные потери и ошибки полностью компенсируются чрезвычайно низкими требованиями к памяти, ограниченным временем выполнения запросов и масштабируемостью - важными факторами для приложений Big Data.

Книга раскрывает общие идеи и важные области применения вероятностных структур данных, включая запросы о принадлежности, подсчёт количества элементов и их частоты, анализ потоковых данных и оценку сходства.

Специалисты в области технологий, включая архитекторов и разработчиков программного обеспечения, впервые познакомятся с вероятностными структурами данных и новыми алгоритмами, изучат важные теоретические аспекты и узнают об их практическом применении в приложениях Big Data.

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About the Author

Андрей Гахов

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.

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