Publication year: 2021
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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. This volume presents 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.
Subject: Computer science, Big Data analysis, Data correlations, Hidden patterns, Data-driven models, Data engineering developments, Distributed computation, Data parallelization, Algorithm parallelization, GPGPU programming, Database management, Database processing frameworks, Database architectures, Data lakes, Data query optimization strategies, Real-time data processing, Data stream analytics, Fog computing, Edge computing, Artificial Intelligence