Details

Big Data Analytics

Big Data Analytics

Theory, Techniques, Platforms, and Applications

von: Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oguzhan Kalyon

139,09 €

Verlag: Springer
Format: PDF
Veröffentl.: 07.05.2024
ISBN/EAN: 9783031556395
Sprache: englisch

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Beschreibungen

Titelbeschreibung

<p>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.</p><p>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.<br></p><p></p>

Inhaltsverzeichnis

<p>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.<br></p><p></p>

Autorenportrait

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 Experience (Wiley), and an Associate Editor for Frontier in Internet of Things. He has also served as a Guest Editor for IEEE Transaction on Industrial Informatics, IEEE Wireless Communications, IEEE Network, Neural Computing and Applications (Springer), Computer Communications (Elsevier), and Transactions on Emerging Telecommunications (Wiley). The main theme of his research is energy-efficient, resilient and intelligent surfaces (smart city, smart grid, IoT-Edge-Cloud systems, healthcare systems, drones). He published several research papers in the top tier transactions and journals (like, IEEE TKDE, IEEE TDSC, IEEE TVT, IEEE TNSM, IEEE TNSE, IEEE TITS, IEEE TSC, IEEE TCC, IEEE TSuSC, IEEE TGCN, IEEE TII, IEEE JSAC, IEEE IoTJ, IEEE System Journal, IEEE WCM, IEEE Communication Magazine, IEEE Network, IEEE Consumer Electronics Magazine, IEEE Internet Computing, IEEE IoT Magazine, IEEE Communication Standards Magazine) and conferences (like, IEEE ICC, IEEE Globecom, IEEE WoWMoM, IEEE Infocom, ACM Mobicom, ACM MobiHoc).<br>

Back cover copy

<p>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.</p><p>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.</p>

Sonderbeitrag

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