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＜電子ブック＞
Big Data Analytics : Theory, Techniques, Platforms, and Applications / by Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon

版	1st ed. 2024.
出版者	(Cham : Springer Nature Switzerland : Imprint: Springer)
出版年	2024
本文言語	英語
大きさ	XXIII, 284 p. 81 illus., 78 illus. in color : online resource
著者標目	*Demirbaga, Ümit author Aujla, Gagangeet Singh author Jindal, Anish author Kalyon, Oğuzhan author SpringerLink (Online service)
件　名	LCSH:Big data LCSH:Quantitative research LCSH:Electric power distribution LCSH:Medical care LCSH:Machine learning FREE:Big Data FREE:Data Analysis and Big Data FREE:Energy Grids and Networks FREE:Health Care FREE:Machine Learning
一般注記	Introduction -- Big Data -- Big Data Analytics -- Cloud Computing for Big Data Analytics -- Big Data Analytics Platforms -- Big Data Storage Solutions -- Big Data Monitoring -- Debugging Big Data Systems for Big Data Analytics -- Machine Learning for Big Data Analytics -- Real-World Big Data Analytics Case Studies -- Big Data Analytics in Smart Grids -- Big Data Analytics in Bioinformatics 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 HTTP:URL=https://doi.org/10.1007/978-3-031-55639-5

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