Big Data Analytics: Theory, Techniques, Platforms, and Applications (Springerbriefs in Applied Sciences and Technology). Este artículo no está disponible.
Idioma: inglés
Editorial: Springer, 2024
Serie: Libro 344 de 472 - SpringerBriefs in Applied Sciences and Technology
- Tapa dura
- Nuevo

Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
Vendedor de IberLibro desde 25 de marzo de 2015
Condición: Nuevo
EUR 152,64
Descripción del artículo del vendedor
In English.
N° de ref. del artículo ria9783031556388_new
- Título
- Big Data Analytics: Theory, Techniques, Platforms, and Applications (Springerbriefs in Applied Sciences and Technology)
- Autor
- Demirbaga, Ümit; Aujla, Gagangeet Singh; Jindal, Anish; Kalyon, Oğuzhan
- Editorial
- Springer
- Año de publicación
- 2024
- Estado
- New
- Encuadernación
- Encuadernación de tapa dura
- Idioma
- inglés
- ISBN 10
- 3031556380
- ISBN 13
- 9783031556388
- Peso del artículo
- 732 gramos
- Serie
- Libro 344 de 472: SpringerBriefs in Applied Sciences and Technology
This book introduces readers to big data analytics. It covers the background to and the concepts of big data, big data analytics, and cloud computing, along with the process of setting up, configuring, and getting familiar with the big data analytics working environments in the first two chapters. The third chapter provides comprehensive information on big data processing systems - from installing these systems to implementing real-world data applications, along with the necessary codes. The next chapter dives into the details of big data storage technologies, including their types, essentiality, durability, and availability, and reveals their differences in their properties. The fifth and sixth chapters guide the reader through understanding, configuring, and performing the monitoring and debugging of big data systems and present the available commercial and open-source tools for this purpose. Chapter seven gives information about a trending machine learning, Bayesian network: a probabilistic graphical model, by presenting a real-world probabilistic application to understand causal, complex, and hidden relationships for diagnosis and forecasting in a scalable manner for big data. Special sections throughout the eighth chapter present different case studies and applications to help the readers to develop their big data analytics skills using various big data analytics frameworks.
The book will be of interest to business executives and IT managers as well as university students and their course leaders, in fact all those who want to get involved in the big data world.
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
“Acerca de” puede pertenecer a otra edición de este título.