Silvina Caíno-Lores

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The increasing amount of data related to the execution of scientific workflows has raised awareness of their shift towards parallel data-intensive problems. In this paper, we deliver our experience with combining the traditional highperformance computing and grid-based approaches for scientific workflows, with Big Data analytics paradigms. Our goal was to(More)
Nowadays there is a raising interest in bridging the gap between Big Data application models and data-intensive HPC. This work explores the effects that Big Data-inspired paradigms could have in current scientific applications through the evaluation of a real-world application from the hydrology domain. This evaluation led to experience that portrayed the(More)
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