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Big Data Analytics : Theory, Techniques, Platforms, and Applications / by Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon.
Springer Nature - Springer Mathematics and Statistics eBooks 2024 English International Available online
View online- Format:
- Book
- Author/Creator:
- Demirbaga, Ümit.
- Series:
- Mathematics and Statistics Series
- Language:
- English
- Subjects (All):
- Big data.
- Quantitative research.
- Electric power distribution.
- Medical care.
- Machine learning.
- Big Data.
- Data Analysis and Big Data.
- Energy Grids and Networks.
- Health Care.
- Machine Learning.
- Local Subjects:
- Big Data.
- Data Analysis and Big Data.
- Energy Grids and Networks.
- Health Care.
- Machine Learning.
- Physical Description:
- 1 online resource (299 pages)
- Edition:
- 1st ed. 2024.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
- Summary:
- 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.
- Contents:
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 9783031556395
- 3031556399
- OCLC:
- 1434176125
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