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    Locality-aware scientific workflow engine for fast-evolving spatiotemporal sensor data, A

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    http://hdl.handle.net/10217/181467
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    KachikaranArulswamy_colostate_0053N_14205.pdf (1022.Kb)
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    Abstract
    Discerning knowledge from voluminous data involves a series of data manipulation steps. Scientists typically compose and execute workflows for these steps using scientific workflow management systems (SWfMSs). SWfMSs have been developed for several research communities including but not limited to bioinformatics, biology, astronomy, computational science, and physics. Parallel execution of workflows has been widely employed in SWfMSs by exploiting the storage and computing resources of grid and cloud services. However, none of these systems have been tailored for the needs of spatiotemporal ...
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    Author(s)
    Kachikaran Arulswamy, Johnson Charles

    Advisor(s)
    Pallickara, Sangmi Lee

    Date Issued
    2017
    Format
    born digital; masters theses
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    • 2000-2019 - CSU Theses and Dissertations
    • Theses and Dissertations - Department of Computer Science

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