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Cloud computing for geospatial big d...

Das, Himansu.

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Cloud computing for geospatial big data analytics = intelligent edge, fog and mist computing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cloud computing for geospatial big data analytics/ edited by Himansu Das ... [et al.].
Reminder of title:	intelligent edge, fog and mist computing /
other author:	Das, Himansu.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xii, 289 p. :ill., digital ;24 cm.
[NT 15003449]:	Big Data Scientific Workflows in the Cloud: Challenges and Future Prospects -- Trust Model based Scheduling of Stochastic Workflows in Cloud and Fog Computing -- Trust-Based Access control in Cloud Computing using Machine Learning -- Cloud Security Ontology (CSO) -- Cloud Based Supply Chain Networks - Principles and Practices -- Parallel Computation of a MMDBM algorithm on GPU mining with Big data -- Data Analytics of IoT Enabled Smart Energy Meter in Smart Cities -- A New and Secure Intrusion Detecting system for Detection of Anomalies within the Big Data -- Geospatial Big Data, Analytics and IoT: Challenges, Applications and Potential -- Geocloud4GI: Cloud SDI Model for Geographical Indications Information Infrastructure Network -- The Role of Geospatial Technology with IoT for Precision Agriculture.
Contained By:	Springer eBooks
Subject:	Geospatial data - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-030-03359-0
ISBN:	9783030033590

Cloud computing for geospatial big data analytics = intelligent edge, fog and mist computing /
Cloud computing for geospatial big data analyticsintelligent edge, fog and mist computing /[electronic resource] :edited by Himansu Das ... [et al.]. - Cham :Springer International Publishing :2019. - xii, 289 p. :ill., digital ;24 cm. - Studies in big data,v.492197-6503 ;. - Studies in big data ;v.49..

Big Data Scientific Workflows in the Cloud: Challenges and Future Prospects -- Trust Model based Scheduling of Stochastic Workflows in Cloud and Fog Computing -- Trust-Based Access control in Cloud Computing using Machine Learning -- Cloud Security Ontology (CSO) -- Cloud Based Supply Chain Networks - Principles and Practices -- Parallel Computation of a MMDBM algorithm on GPU mining with Big data -- Data Analytics of IoT Enabled Smart Energy Meter in Smart Cities -- A New and Secure Intrusion Detecting system for Detection of Anomalies within the Big Data -- Geospatial Big Data, Analytics and IoT: Challenges, Applications and Potential -- Geocloud4GI: Cloud SDI Model for Geographical Indications Information Infrastructure Network -- The Role of Geospatial Technology with IoT for Precision Agriculture.

This book introduces the latest research findings in cloud, edge, fog, and mist computing and their applications in various fields using geospatial data. It solves a number of problems of cloud computing and big data, such as scheduling, security issues using different techniques, which researchers from industry and academia have been attempting to solve in virtual environments. Some of these problems are of an intractable nature and so efficient technologies like fog, edge and mist computing play an important role in addressing these issues. By exploring emerging advances in cloud computing and big data analytics and their engineering applications, the book enables researchers to understand the mechanisms needed to implement cloud, edge, fog, and mist computing in their own endeavours, and motivates them to examine their own research findings and developments.

ISBN: 9783030033590

Standard No.: 10.1007/978-3-030-03359-0doiSubjects--Topical Terms:

1066517
Geospatial data
--Data processing.

LC Class. No.: G70.217.G46 / C568 2019

Dewey Class. No.: 006.3

Cloud computing for geospatial big data analytics = intelligent edge, fog and mist computing /
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245 0 0 $a Cloud computing for geospatial big data analytics $h [electronic resource] : $b intelligent edge, fog and mist computing / $c edited by Himansu Das ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xii, 289 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.49
505 0 $a Big Data Scientific Workflows in the Cloud: Challenges and Future Prospects -- Trust Model based Scheduling of Stochastic Workflows in Cloud and Fog Computing -- Trust-Based Access control in Cloud Computing using Machine Learning -- Cloud Security Ontology (CSO) -- Cloud Based Supply Chain Networks - Principles and Practices -- Parallel Computation of a MMDBM algorithm on GPU mining with Big data -- Data Analytics of IoT Enabled Smart Energy Meter in Smart Cities -- A New and Secure Intrusion Detecting system for Detection of Anomalies within the Big Data -- Geospatial Big Data, Analytics and IoT: Challenges, Applications and Potential -- Geocloud4GI: Cloud SDI Model for Geographical Indications Information Infrastructure Network -- The Role of Geospatial Technology with IoT for Precision Agriculture.
520 $a This book introduces the latest research findings in cloud, edge, fog, and mist computing and their applications in various fields using geospatial data. It solves a number of problems of cloud computing and big data, such as scheduling, security issues using different techniques, which researchers from industry and academia have been attempting to solve in virtual environments. Some of these problems are of an intractable nature and so efficient technologies like fog, edge and mist computing play an important role in addressing these issues. By exploring emerging advances in cloud computing and big data analytics and their engineering applications, the book enables researchers to understand the mechanisms needed to implement cloud, edge, fog, and mist computing in their own endeavours, and motivates them to examine their own research findings and developments.
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650 0 $a Geographic information systems. $3 542758
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650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Geographical Information Systems/Cartography. $3 890881
650 2 4 $a Big Data. $3 3134868
700 1 $a Das, Himansu. $3 3383321
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830 0 $a Studies in big data ; $v v.49. $3 3383322
856 4 0 $u https://doi.org/10.1007/978-3-030-03359-0
950 $a Intelligent Technologies and Robotics (Springer-42732)