Название: Research Practitioner's Handbook on Big Data Analytics
Автор: S. Sasikala, Renuka Devi D., Raghvendra Kumar
Издательство: CRC Press/Apple Academic Press
Год: 2023
Страниц: 310
Язык: английский
Формат: pdf (true)
Размер: 27.9 MB

This new volume addresses the growing interest in and use of Big Data analytics in many industries and in many research fields around the globe; it is a comprehensive resource on the core concepts of Big Data analytics and the tools, techniques, and methodologies. The book gives the why and the how of Big Data analytics in an organized and straightforward manner, using both theoretical and practical approaches.

The book’s authors have organized the contents in a systematic manner, starting with an introduction and overview of Big Data analytics and then delving into pre-processing methods, feature selection methods and algorithms, Big Data streams, and Big Data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, Big Data streams, and many more are discussed. The authors explain how Deep Learning and Machine Learning along with other methods and tools are applied in Big Data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media.

This book would be a complete and comprehensive handbook in the research domain of Big Data analytics.

Chapter 1 briefs about the fundamentals of big data, terminologies, types of analytics, and big data tools and techniques.
Chapter 2 outlines the need for preprocessing data and various methods in handling the same. Both text and image preprocessing methods are also highlighted. In addition to that, challenges of streaming data processing are also discussed.
Chapter 3 briefs on various featured selection methods and algorithms, and research problems related to each category are discussed with specific examples.
Chapter 4 describes the core methods of big data streams and the prerequisite for parallelization. This chapter also enlightens on the streaming architecture. Hadoop architecture is comprehensively mentioned with the components of parallel processing.
Chapter 5 updates on the big data classification techniques, and various learning methodologies are explained with examples. To extend the same, deep learning algorithms and architectures are also briefed.
Chapter 6 highlights application across verticals with research problems and solutions.






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