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Environmental research infrastructures (RIs) support data-intensive research by integrating large-scale sensor/observer networks with dedicated data curation services and analytical tools. However the diversity of scientific disciplines coupled with the lack of an accepted methodology for constructing new RIs inevitably leads to incompatibilities between the data models, metadata standards and service...
Many scientific workflow applications are data intensive. Large quantities of simulation generated data or data collected from sensors or instruments drive the process flow of the applications, and the processing of the data is commonly done at a different location from where the data is stored. Advanced networks such as hybrid networks make it feasible for high level applications to request network...
Quality of the service (QoS) plays an important role in the life-cycle of scientific workflows for composing and executing applications. However, the quality of network services has so far rarely been considered in composing and executing scientific workflows. Currently, scientific applications tune the execution quality neglecting network resources, and by selecting only optimal software services...
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