Mussel farm product quality remains a challenging problem for operational marine science. In an operational scenario, the model chain, orchestrated in a workflow fashion, produces a huge amount of predicted spatially-referenced (big) data. These workflow components have been integrated into the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT), a workflow engine and data science portal based on Galaxy and Globus technologies. We describe how FACE-IT workflows can be used to couple many simulation/prediction models, leveraging high-performance and cloud computing resources to enable fast full system modeling in order to produce operational predictions about the impact of pollutants spilled out from both natural and anthropic sources in mussels farming high density areas.

Applications of the FACE-IT portal and workflow engine for operational food quality prediction and assessment: Mussel farm monitoring in the Bay of Napoli, Italy

MONTELLA, Raffaele
;
DI LUCCIO, DIANA;RICCIO, Angelo;
2016

Abstract

Mussel farm product quality remains a challenging problem for operational marine science. In an operational scenario, the model chain, orchestrated in a workflow fashion, produces a huge amount of predicted spatially-referenced (big) data. These workflow components have been integrated into the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT), a workflow engine and data science portal based on Galaxy and Globus technologies. We describe how FACE-IT workflows can be used to couple many simulation/prediction models, leveraging high-performance and cloud computing resources to enable fast full system modeling in order to produce operational predictions about the impact of pollutants spilled out from both natural and anthropic sources in mussels farming high density areas.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/59867
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