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Big Data Infrastructure Technologies for Data Analytics de Yuri Demchenko

Big Data Infrastructure Technologies for Data Analytics

Autor Yuri Demchenko, Juan J. Cuadrado-Gallego, Oleg Chertov, Marharyta Aleksandrova
en Limba Engleză Hardback – 26 oct 2024
This book provides a comprehensive overview and introduction to Big Data Infrastructure technologies, existing cloud-based platforms, and tools for Big Data processing and data analytics, combining both a conceptual approach in architecture design and a practical approach in technology selection and project implementation.
Readers will learn the core functionality of major Big Data Infrastructure components and how they integrate to form a coherent solution with business benefits. Specific attention will be given to understanding and using the major Big Data platform Apache Hadoop ecosystem, its main functional components MapReduce, HBase, Hive, Pig, Spark and streaming analytics.   The book includes topics related to enterprise and research data management and governance and explains modern approaches to cloud and Big Data security and compliance.
The book covers two knowledge areas defined in the EDISON Data Science Framework (EDSF): Data Science Engineering and Data Management and Governance and can be used as a textbook for university courses or provide a basis for practitioners for further self-study and practical use of Big Data technologies and competent evaluation and implementation of practical projects in their organizations.
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Specificații

ISBN-13: 9783031693656
ISBN-10: 3031693655
Pagini: 560
Ilustrații: X, 546 p. 138 illus. in color.
Dimensiuni: 160 x 241 x 36 mm
Greutate: 1 kg
Ediția:2024
Editura: Springer
Locul publicării:Cham, Switzerland

Cuprins

Chapter 1 Artificial Intelligence for Sustainable Ocean Health.- Chapter 2 A Comprehensive Study of AI (XAI) for Ocean Health Monitoring.- Chapter 3 EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR OCEAN HEALTH: APPLICATIONS AND CHALLENGES.- Chapter 4 Intelligent Hash Function Based Key-Exchange Scheme for Ocean Underwater Data Transmission.- Chapter 5 Utilization of Underwater Wireless Sensor Network Through Supervising a Random Network Environment in the Ocean Environment .- Chapter 6 Revolutionizing Internet of Underwater Things with Federated Learning.- Chapter 7Marine Resources: Identification, Restoring & Monitoring of Fisheries Food Resources Using Deep Learning and Image Processing.- Chapter 8 Review on the Optics and Photonics in Environmental Sustainability.- Chapter 9 Leveraging the Power of AI for Sustainable Oceans.- Chapter 10 Role of pre–Preprocessing Algorithm in the Underwater Image Analysis.- Chapter 11Blue Ocean and Machine Learning Trajectories SDG 14- Life below Water for Handling Ocean Pollution: Metaverse Conserve Ocean Health Sustainability through the Lens of Transboundary Legal-Policy Regulations as Articulating Space for Futuristic Changes.- Chapter 12 Role of Artificial Intelligence and Technologies in Improving Ocean Health in Promoting Tourism.- Chapter 13 Sustainable Development Goal14 Explainable AI (XAI) for Ocean Health.- Chapter 14 Explainable AI (XAI) for Ocean Health: Exploring the Role of Explainable AI in Enhancing Ocean Health.- Chapter 15 Federated Learning for Internet of Underwater Drone Things.- Chapter 16 ARTIFICIAL INTELLIGENCE AS KEYENABLER FOR SAFEGUARDING THE MARINE RESOURCES.- Chapter 17 Enhancing Underwater Imagery with AI/ML and IoT in ROV Technology.- Chapter 18 REVOLUTIONIZING OCEAN CLEANUP: AI AND ROBOTICS TACKLE POLLUTION CHALLENGES.

Notă biografică

Dr. Juan José Associate Professor in the Department of Computer Science at the University of Alcalá, in the area of ¿¿Computer Science and Artificial Intelligence and Affiliate Associate Professor in the Department of Computer Science and Software Engineering, of the Faculty of Engineering and Computer Science, of the Concordia University, in Montreal, Canada. Previously, he was a professor at the Spanish Universities Universitat Oberta de Catalunya, in Barcelona, ¿¿from 2004 to 2016, the University of Valladolid, in Segovia, in 2004, and the Universidad Carlos III de Madrid, in Madrid, between 1997 and 2004. He has been Visiting Associate Professor, in the Department of Software and IT Engineering, of the École de Technologie Supérieure, at the Université du Québec à Montréal, in Montreal, Canada, from 2009 to 2015; and Visiting Professor, in the Postgraduate and Research section, of the Faculty of Administration and Management, of the National Polytechnic Institute, inMexico City, Mexico, from 2009 to 2014. He was also a researcher in the Department of Astrophysics and Atmospheric Sciences, from the Faculty of Physical Sciences, of the Complutense University of Madrid, in Madrid, Spain, from 1994 to 1997. Juan José has a degree in Physical Sciences from the Complutense University of Madrid, in 1994; obtained in Recognition of the research sufficiency in the Faculty of Physical Sciences of the Complutense University of Madrid, in 1997; and the Doctorate in Computer Engineering, at the Carlos III University of Madrid, in 2001, with the qualification of A "cum laude" unanimously by the court. It currently has 4 six-year periods and 3 five-year periods. In 2010, she obtained the Outstanding Research Pathway certification by the National Agency for Evaluation and Prospective (ANEP) of the Secretary of State for Universities and Research of the Ministry of Science and Innovation, within the program I3 Program, Incentive for the Incorporation andIntensification of Research Activity. Juan José has carried out research stays at the Universities: University of Amsterdam, Amsterdam, Holland, at the Informatics Institute, of the Faculty of Science, in 2018, funded by a mobility grant from the University of Alcalá; at the Otto-von-Guericke-University, Magdeburg, Germany, at the Institüt für Verteilte Systeme, de la Fakültat für Informatik, in 2013, funded by a mobility grant from the University of Alcalá, in 2012, within a sabbatical year granted by the University of Alcalá, in 2009, funded by a "José Castillejo" for further studies and research, from the University of Alcalá; at the Université du Québec à Montréal, in Montreal, Canada, in the Department of Software and IT Engineering, from the École de Technologie Supérieure, in 2006 and 2005; at the University of Reading, in Reading, United Kingdom, in the Computer Science Department, in 2005 and 2004; and the Università Roma Tre, in Rome, Italy, in the Dipartamento di Informatica e Automatizacione, in 2004 and 2003. Juan José is currently researching in the fields of Artificial Intelligence and Data Science. He has made more than 200 scientific publications, many of which have been in journals indexed in the JRC Science Edition. He has also participated, as principal investigator or researcher, in numerous research projects, both financed with public funding, both European, national, regional or university; as well as with private financing, through contracts made through article 83 of the University Law. He has also directed nine doctoral theses, all of them having received the highest qualification; and has participated in numerous doctoral courts, in Spain, Germany, and Mexico. He is also an External Evaluator of projects in Computer Science, of the Natural Sciences and Engineering Research Council of Canada since 2014 and Evaluator of the National Agency for Evaluation and Prospective, of the General Directorate of Scientific and TechnicalResearch of Spain, of the Minis

Textul de pe ultima copertă

This book provides a comprehensive overview and introduction to Big Data Infrastructure technologies, existing cloud-based platforms, and tools for Big Data processing and data analytics, combining both a conceptual approach in architecture design and a practical approach in technology selection and project implementation.
Readers will learn the core functionality of major Big Data Infrastructure components and how they integrate to form a coherent solution with business benefits. Specific attention will be given to understanding and using the major Big Data platform Apache Hadoop ecosystem, its main functional components MapReduce, HBase, Hive, Pig, Spark and streaming analytics.   The book includes topics related to enterprise and research data management and governance  and explains modern approaches to cloud and Big Data security and compliance.
The book covers two knowledge areas defined in the EDISON Data Science Framework (EDSF): Data Science Engineering and Data Management and Governance and can be used as a textbook for university courses or provide a basis for practitioners for further self-study and practical use of Big Data technologies and competent evaluation and implementation of practical projects in their organizations.

Caracteristici

Explains how modern cloud-based platforms and tools can be integrated to form a coherent solution with business benefits Reveals the major Big Data infrastructure technologies that data science and analytics developers need to know Useful for students to practitioners for competent evaluation and implementation of Big Data projects in organizations