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Handbook of Big Data Analytics Volume 2: Applications in ICT, security and business analytics

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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: security of data; data analysis; Internet of Things; financial data processing; software defined networking; data privacy; business data processing; neural nets; recommender systems; behavioural sciences computing; Big Data; stock markets

Other keywords: parallel self-organizing maps; ICT; Apache Spark; ciphertext-policy attribute-based signcryption; e-commerce; stock market movement prediction; bank customer complaints; contract-driven financial reporting; banking analytics; privacy-preserving techniques; parallel hierarchical clustering; security intelligence; secure big data storage; evolving spiking neural networks; zero attraction data selective adaptive filtering algorithm; parallelized radial basis function neural network; big data regression; cloud storage; wavelet neural network; traffic prediction; IoT data streams; visual sentiment analysis; recommender systems; secure routing; big data analytics; algorithmic contracts; gender-based classification; software defined networking; automated analytics pipelines; GPU; business analytics; smart city; distributed ledger technology; behaviour analytics; Internet of Things

Subjects: General and management topics; Data handling techniques; Other DBMS; Social and behavioural sciences computing; Business and administration; Computer networks and techniques; Search engines; Information networks

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