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Big Data Analytics, 1st ed. 2024 Theory, Techniques, Platforms, and Applications

Langue : Anglais

Auteurs :

Couverture de l’ouvrage Big Data Analytics

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.

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.

Dr. Gagangeet Singh Aujla [Senior Member, IEEE] is an Assistant Professor of Computer Science at Durham University, UK and a Fellow of Durham Energy Institute. Before this, he worked as a post-doctoral research associate at Newcastle University, a research associate at Thapar University (India), a visiting researcher at the University of Klagenfurt (Austria) and in various academic positions for over a decade. He received my PhD from Thapar University (India) and my master’s and bachelor’s degrees from the Punjab Technical University (India). He received the IEEE TCSC Award for Excellence in Scalable Computing (ECR) 2022 for contributions on research and development of sustainable edge-cloud continuum for resource-constrained smart environments. He also received "Early-Career Award 2022 (Runner-up)" from the IEEE TEMS TC on Blockchain and Distributed Ledger Technologies for contributions to the research and teaching in blockchain and distributed ledger technologies. He received the 2018 IEEE TCSC Outstanding PhD Dissertation Award for contributions to designing and developing methods for self-sustainable cloud data centres. He also received 2021 IEEE Systems Journal Best Paper Award and TIET Best Paper Award for his research articles. He worked on various funded research projects awarded by UKRI, EPSRC, the Department of Science and Technology (India), and the Austrian Federal Ministry of Education, Science and Research. He serves as Co-Secretary, IEEE UK and Ireland Diversity, Equality and Inclusion Committee and Environment Champion (Durham Greenscpace). He led the team organizing workshops (BlockSecSDN, BlockCPS, SecSDN and EdgeAI) with different IEEE Communication Society conferences like IEEE Infocom, IEEE Globecom, IEEE ICC, ACM/IEEE UCC and many more. Contributing to the research community, he serves as an Area Editor for Ad hoc Networks (Elsevier), an Associate Editor for IET Smart Grid, an Associate Editor for Concurrency and Computation: Practice and Expe

Explains how to handle big data clearly and comprehensibly and indicates the best tools for big data analysis

Describes big data systems themselves and discusses how to monitor and debug big data systems

Contains case studies in healthcare, smart grids and other sectors

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Ouvrage de 294 p.

15.5x23.5 cm

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