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Fog Computing, Deep Learning and Big Data Analytics-Research Directions / by C.S.R. Prabhu

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: Singapore : Springer Singapore, 2019Description: Online-Ressource (XIII, 71 p. 5 illus., 1 illus. in color, online resource)ISBN:
  • 9789811332098
Subject(s): Additional physical formats: 9789811332081 | 9789811332104 | Printed edition: 9789811332081 | Printed edition: 9789811332104 DDC classification:
  • 005.7
LOC classification:
  • QA76.9.B45
DOI: DOI: 10.1007/978-981-13-3209-8Online resources: Summary: 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 solutionsSummary: Introduction -- Fog Application management -- Fog Analytics -- Fog Security and Privary -- Research Directions -- ConclusionPPN: PPN: 1048364461Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SEB | ZDB-2-SXCS
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