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Big data analytics = theory, techniques, platforms, and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Big data analytics/ by Ümit Demirbaga ... [et al.].
其他題名:	theory, techniques, platforms, and applications /
其他作者:	Demirbaga, Ümit.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xxiii, 284 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Big data. -
電子資源:	https://doi.org/10.1007/978-3-031-55639-5
ISBN:	9783031556395

Big data analytics = theory, techniques, platforms, and applications /
Big data analyticstheory, techniques, platforms, and applications /[electronic resource] :by Ümit Demirbaga ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xxiii, 284 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9783031556395

Standard No.: 10.1007/978-3-031-55639-5doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Big data analytics = theory, techniques, platforms, and applications /
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520 $a 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.
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