Supporting Data-Driven Workflows Enabled by Large Scale Observatories
| Autor: | Moustafa AbdelBaky, Ivan Rodero, Manish Parashar, Ali Reza Zamani, Daniel Balouek-Thomert |
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| Rok vydání: | 2017 |
| Předmět: |
020203 distributed computing
Data processing Distributed database Computer science Quality of service Scale (chemistry) 02 engineering and technology Data science Data-driven Workflow Data access Ocean Observatories Initiative 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | eScience |
| DOI: | 10.1109/escience.2017.95 |
| Popis: | Large scale observatories are shared-use resources that provide open access to data from geographically distributed sensors and instruments. This data has the potential to accelerate scientific discovery. However, seamlessly integrating the data into scientific workflows remains a challenge. In this paper, we summarize our ongoing work in supporting data-driven and data-intensive workflows and outline our vision for how these observatories can improve large-scale science. Specifically, we present programming abstractions and runtime management services to enable the automatic integration of data in scientific workflows. Further, we show how approximation techniques can be used to address network and processing variations by studying constraint limitations and their associated latencies. We use the Ocean Observatories Initiative (OOI) as a driving use case for this work. |
| Databáze: | OpenAIRE |
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