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.
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