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and P.L.D.J. contributed to the design and execution of data generation with the experimental pipeline.
We summarize the design and execution of data collections, identify key challenges, and convey some lessons learned.
In transforming structural monitoring systems into structural health monitoring systems, sensing systems support the automated execution of data interrogation methods that diagnose damage including its location and severity.
Dryad is a research project at Microsoft Research for the general-purpose runtime for execution of data parallel applications.
Rather, it should be viewed as instilling responsibility for the development and execution of data processing systems [8] such as services offered by cloud.
The MapReduce paradigm is probably the most widely adopted approach today to simplify the development and parallel execution of data processing jobs.
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Panda (for Provenance and Data) is a system that supports the creation and execution of data-oriented workflows, with automatic provenance generation and built-in provenance tracing operations.
Performant execution of data-parallel jobs needs good execution plans.
In general, in order to ease the parallel execution of data-intensive applications input data is divided into smaller data chunks that can be processed separately.
From this perspective, optimizing the execution of data-intensive bioinformatics workflows in hybrid clouds is an interesting problem.
In this work, we investigate the problem of optimizing the parallel execution of data-intensive bioinformatics workflows in hybrid cloud environments.
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submission of data
continuation of data
completion of data
realization of data
enforcement of data
conclusion of data
turnaround of data
implementation of data
delivery of data
pursuit of data
compliance of data
signature of data
performance of data
utilisation of data
submissions of data
execution of notification
execution of seizure
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