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dc.contributor.authorLiu, Jia
dc.contributor.authorXue, Yong
dc.contributor.authorPalmer-Brown, Dominic
dc.contributor.authorChen, Ziqiang
dc.contributor.authorHe, Xingwei
dc.date.accessioned2017-05-10T16:57:20Z
dc.date.available2017-05-10T16:57:20Z
dc.date.issued2015-11-13
dc.identifier.citationLiu, J. et al (2015) 'High-Throughput Geocomputational Workflows in a Grid Environment', Computer, 48 (11):70en
dc.identifier.issn00189162
dc.identifier.doi10.1109/MC.2015.331
dc.identifier.urihttp://hdl.handle.net/10545/621606
dc.description.abstractA grid-computing platform facilitates geocomputational workflow composition to process big geosciences data while fully using idle resources to accelerate processing speed. An experiment with aerosol optical depth retrieval from satellite data shows a 25 percent improvement in runtime over a single high-performance computer.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7328638/en
dc.rightsArchived with thanks to Computeren
dc.subjectRemote sensingen
dc.subjectData modelsen
dc.subjectProcessor schedulingen
dc.subjectGeospatial analysisen
dc.subjectComputational modelingen
dc.subjectAerosol optical depthen
dc.subjectGrid computingen
dc.titleHigh-throughput geocomputational workflows in a grid environmenten
dc.typeArticleen
dc.contributor.departmentChinese Academy of Sciencesen
dc.contributor.departmentLondon Metropolitan Universityen
dc.identifier.journalComputeren
html.description.abstractA grid-computing platform facilitates geocomputational workflow composition to process big geosciences data while fully using idle resources to accelerate processing speed. An experiment with aerosol optical depth retrieval from satellite data shows a 25 percent improvement in runtime over a single high-performance computer.


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