Handbook of Big Data Analytics: Volume 1
Methodologies
Published on 3. September 2021
Book
Hardback
391 pages
978-1-83953-064-7 (ISBN)
Description
Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time.
In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data.
The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting.
The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.
In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data.
The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting.
The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 239 mm
Width: 160 mm
Thickness: 23 mm
Weight
794 gr
ISBN-13
978-1-83953-064-7 (9781839530647)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Vadlamani Ravi is a professor at the Institute for Development and Research in Banking Technology, Hyderabad, where he spearheads the Center of Excellence in Analytics, the first-of-its-kind in India. He has over 32 years of experience in research and teaching. He is on the Editorial Board several international journals. He has published more than 230 papers in international journals, conferences and book chapters.
Aswani Kumar Cherukuri is a professor of the School of Information Technology and Engineering at Vellore Institute of Technology, India. He has almost 20 years of academic and research experience. His research interests include machine learning and information security. He has published more than 150 research papers in various journals and conferences, and executed major research projects funded by Govt. of India. He is a senior member of ACM and life member of CSI, ISTE.
Aswani Kumar Cherukuri is a professor of the School of Information Technology and Engineering at Vellore Institute of Technology, India. He has almost 20 years of academic and research experience. His research interests include machine learning and information security. He has published more than 150 research papers in various journals and conferences, and executed major research projects funded by Govt. of India. He is a senior member of ACM and life member of CSI, ISTE.
Content
Chapter 1: The impact of Big Data on databases
Chapter 2: Big data processing frameworks and architectures: a survey
Chapter 3: The role of data lake in big data analytics: recent developments and challenges
Chapter 4: Query optimization strategies for big data
Chapter 5: Toward real-time data processing: an advanced approach in big data analytics
Chapter 6: A survey on data stream analytics
Chapter 7: Architectures of big data analytics: scaling out data mining algorithms using Hadoop-MapReduce and Spark
Chapter 8: A review of fog and edge computing with big data analytics
Chapter 9: Fog computing framework for Big Data processing using cluster management in a resource-constraint environment
Chapter 10: Role of artificial intelligence and big data in accelerating accessibility for persons with disabilities
Overall conclusions
Chapter 2: Big data processing frameworks and architectures: a survey
Chapter 3: The role of data lake in big data analytics: recent developments and challenges
Chapter 4: Query optimization strategies for big data
Chapter 5: Toward real-time data processing: an advanced approach in big data analytics
Chapter 6: A survey on data stream analytics
Chapter 7: Architectures of big data analytics: scaling out data mining algorithms using Hadoop-MapReduce and Spark
Chapter 8: A review of fog and edge computing with big data analytics
Chapter 9: Fog computing framework for Big Data processing using cluster management in a resource-constraint environment
Chapter 10: Role of artificial intelligence and big data in accelerating accessibility for persons with disabilities
Overall conclusions