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Handbook of Big Data Analytics. Volume 1: Methodologies

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Editors: Vadlamani Ravi 1 ; Aswani Kumar Cherukuri 2
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Publication Year: 2021

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.

Inspec keywords: query processing; handicapped aids; data analysis; database management systems; data mining; Big Data; parallel processing

Other keywords: data stream analytics; edge computing; fog computing; data lake; artificial intelligence; Hadoop-MapReduce; query optimization strategies; real-time data processing; disabled person accessibility; Spark; resource-constraint environment; big data processing; cluster management; data mining algorithms; databases

Subjects: General and management topics; Database management systems (DBMS); Computer assistance for persons with handicaps; Parallel software; Data handling techniques

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