001     865715
005     20210130003127.0
024 7 _ |a 2128/23964
|2 Handle
037 _ _ |a FZJ-2019-05050
041 _ _ |a English
100 1 _ |a Memon, Mohammad Shahbaz
|0 P:(DE-Juel1)132190
|b 0
|e Corresponding author
245 _ _ |a Standards-based Models and Architectures to Automate Scalable and Distributed Data Processing and Analysis
|f - 2019-10-04
260 _ _ |c 2019
300 _ _ |a 102 pp.
336 7 _ |a Output Types/Dissertation
|2 DataCite
336 7 _ |a DISSERTATION
|2 ORCID
336 7 _ |a PHDTHESIS
|2 BibTeX
336 7 _ |a Thesis
|0 2
|2 EndNote
336 7 _ |a Dissertation / PhD Thesis
|b phd
|m phd
|0 PUB:(DE-HGF)11
|s 1579675342_9791
|2 PUB:(DE-HGF)
336 7 _ |a doctoralThesis
|2 DRIVER
502 _ _ |a Dissertation, University of Iceland, 2019
|c University of Iceland
|b Dissertation
|d 2019
520 _ _ |a Scientific communities engaging in big data analysis face numerous challenges in managing complex computations and the related data on emerging and distributed computing infrastructures. Large-scale data analysis requires applications with simplified access to multiple resource management systems. Several generic or domain-specific technologies have been developed to exploit diversified computing environments, but due to the heterogeneity of computing and data architectures they are not capable of enabling real science cases. Scientific gateways and workflows are one such example which requires the management of jobs on multiple kinds of batch systems using heterogeneous supercomputing architectures and access to advanced distributed file systems. To support these requirements, a unified architectural framework is presented in this dissertation that coalesces the right combination of standards and adequate middleware realisation. This framework manages concurrent access for diversified user communities through consistent and robust computing and data interfaces oriented to current application and infrastructure demands. The investigations reported in this dissertation were mainly motivated by physical and machine-learning models, represented by two scientific case studies: biophysics and Earth sciences. In the field of biophysics, the UltraScan scientific gateway is enhanced to enable the processing of domain-specific data through standards-based job and data management interfaces in HPC environments. The second domain deals with Earth sciences and automates the processing of machine-learning algorithms (e.g. classification of remote sensing images) using scalable and parallel implementations. As proof of concept, both the case studies are supported through open source implementations, in the form of middleware realisation, client APIs and their integration with state-of-the-art science gateway frameworks.
536 _ _ |a 512 - Data-Intensive Science and Federated Computing (POF3-512)
|0 G:(DE-HGF)POF3-512
|c POF3-512
|f POF III
|x 0
856 4 _ |u https://opinvisindi.is/handle/20.500.11815/1299
856 4 _ |u https://juser.fz-juelich.de/record/865715/files/uiphdthesis_shahbaz_memon20190906_final_for_printing_papers_upscaled_p88_corrected.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/865715/files/uiphdthesis_shahbaz_memon20190906_final_for_printing_papers_upscaled_p88_corrected.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:865715
|p openaire
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910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)132190
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-512
|2 G:(DE-HGF)POF3-500
|v Data-Intensive Science and Federated Computing
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
914 1 _ |y 2019
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a phd
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 1 _ |a FullTexts


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