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020 _a9789811332098
_9978-981-13-3209-8
024 7 _a10.1007/978-981-13-3209-8
_2doi
050 4 _aQA76.9.B45
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.7
_223
100 1 _aPrabhu, C.S.R.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aFog Computing, Deep Learning and Big Data Analytics-Research Directions
_h[electronic resource] /
_cby C.S.R. Prabhu.
250 _a1st ed. 2019.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2019.
300 _aXIII, 71 p. 5 illus., 1 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Fog Application management -- Fog Analytics -- Fog Security and Privary -- Research Directions -- Conclusion.
520 _aThis 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.
650 0 _aBig data.
650 0 _aArtificial intelligence
_xData processing.
650 0 _aInformation technology
_xManagement.
650 1 4 _aBig Data.
650 2 4 _aData Science.
650 2 4 _aComputer Application in Administrative Data Processing.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811332081
776 0 8 _iPrinted edition:
_z9789811332104
856 4 0 _uhttps://doi.org/10.1007/978-981-13-3209-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c173086
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