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With new technologies, such as computer vision, internet of things,
mobile computing, e-governance and e-commerce, and wide
applications of social media, organizations generate a huge volume
of data and at a much faster rate than several years ago. Big data
in large-/small-scale systems, characterized by high volume,
diversity, and velocity, increasingly drives decision making and is
changing the landscape of business intelligence. From governments
to private organizations, from communities to individuals, all
areas are being affected by this shift. There is a high demand for
big data analytics that offer insights for computing efficiency,
knowledge discovery, problem solving, and event prediction. To
handle this demand and this increase in big data, there needs to be
research on innovative and optimized machine learning algorithms in
both large- and small-scale systems. Applications of Big Data in
Large- and Small-Scale Systems includes state-of-the-art research
findings on the latest development, up-to-date issues, and
challenges in the field of big data and presents the latest
innovative and intelligent applications related to big data. This
book encompasses big data in various multidisciplinary fields from
the medical field to agriculture, business research, and smart
cities. While highlighting topics including machine learning, cloud
computing, data visualization, and more, this book is a valuable
reference tool for computer scientists, data scientists and
analysts, engineers, practitioners, stakeholders, researchers,
academicians, and students interested in the versatile and
innovative use of big data in both large-scale and small-scale
systems.
With new technologies, such as computer vision, internet of things,
mobile computing, e-governance and e-commerce, and wide
applications of social media, organizations generate a huge volume
of data and at a much faster rate than several years ago. Big data
in large-/small-scale systems, characterized by high volume,
diversity, and velocity, increasingly drives decision making and is
changing the landscape of business intelligence. From governments
to private organizations, from communities to individuals, all
areas are being affected by this shift. There is a high demand for
big data analytics that offer insights for computing efficiency,
knowledge discovery, problem solving, and event prediction. To
handle this demand and this increase in big data, there needs to be
research on innovative and optimized machine learning algorithms in
both large- and small-scale systems. Applications of Big Data in
Large- and Small-Scale Systems includes state-of-the-art research
findings on the latest development, up-to-date issues, and
challenges in the field of big data and presents the latest
innovative and intelligent applications related to big data. This
book encompasses big data in various multidisciplinary fields from
the medical field to agriculture, business research, and smart
cities. While highlighting topics including machine learning, cloud
computing, data visualization, and more, this book is a valuable
reference tool for computer scientists, data scientists and
analysts, engineers, practitioners, stakeholders, researchers,
academicians, and students interested in the versatile and
innovative use of big data in both large-scale and small-scale
systems.
As the progression of the internet continues, society is finding
easier, quicker ways of simplifying their needs with the use of
technology. With the growth of lightweight devices, such as smart
phones and wearable devices, highly configured hardware is in
heightened demand in order to process the large amounts of raw data
that are acquired. Connecting these devices to fog computing can
reduce bandwidth and latency for data transmission when associated
with centralized cloud solutions and uses machine learning
algorithms to handle large amounts of raw data. The risks that
accompany this advancing technology, however, have yet to be
explored. Architecture and Security Issues in Fog Computing
Applications is a pivotal reference source that provides vital
research on the architectural complications of fog processing and
focuses on security and privacy issues in intelligent fog
applications. While highlighting topics such as machine learning,
cyber-physical systems, and security applications, this publication
explores the architecture of intelligent fog applications enabled
with machine learning. This book is ideally designed for IT
specialists, software developers, security analysts, software
engineers, academicians, students, and researchers seeking current
research on network security and wireless systems.
As the progression of the internet continues, society is finding
easier, quicker ways of simplifying their needs with the use of
technology. With the growth of lightweight devices, such as smart
phones and wearable devices, highly configured hardware is in
heightened demand in order to process the large amounts of raw data
that are acquired. Connecting these devices to fog computing can
reduce bandwidth and latency for data transmission when associated
with centralized cloud solutions and uses machine learning
algorithms to handle large amounts of raw data. The risks that
accompany this advancing technology, however, have yet to be
explored. Architecture and Security Issues in Fog Computing
Applications is a pivotal reference source that provides vital
research on the architectural complications of fog processing and
focuses on security and privacy issues in intelligent fog
applications. While highlighting topics such as machine learning,
cyber-physical systems, and security applications, this publication
explores the architecture of intelligent fog applications enabled
with machine learning. This book is ideally designed for IT
specialists, software developers, security analysts, software
engineers, academicians, students, and researchers seeking current
research on network security and wireless systems.
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