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Fog computing, deep learning and big...

Prabhu, C. S. R.

Fog computing, deep learning and big data analytics-research directions

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fog computing, deep learning and big data analytics-research directionsby C. S. R. Prabhu.
Author:	Prabhu, C. S. R.
Published:	Singapore :Springer Singapore :2019.
Description:	xiii, 71 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Cloud computing.
Online resource:	https://doi.org/10.1007/978-981-13-3209-8
ISBN:	9789811332098$q(electronic bk.)

Fog computing, deep learning and big data analytics-research directions
Prabhu, C. S. R.

Fog computing, deep learning and big data analytics-research directions[electronic resource] /by C. S. R. Prabhu. - Singapore :Springer Singapore :2019. - xiii, 71 p. :ill., digital ;24 cm.

Introduction -- Fog Application management -- Fog Analytics -- Fog Security and Privary -- Research Directions -- Conclusion.

This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.

ISBN: 9789811332098$q(electronic bk.)

Standard No.: 10.1007/978-981-13-3209-8doiSubjects--Topical Terms:

378527
Cloud computing.

LC Class. No.: QA76.585

Dewey Class. No.: 004.6782

Fog computing, deep learning and big data analytics-research directions
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505 0 $a Introduction -- Fog Application management -- Fog Analytics -- Fog Security and Privary -- Research Directions -- Conclusion.
520 $a This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.
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