Journal
2017 IEEE 13TH INTERNATIONAL CONFERENCE ON E-SCIENCE (E-SCIENCE)
Volume -, Issue -, Pages 592-595Publisher
IEEE
DOI: 10.1109/eScience.2017.95
Keywords
Large scale observatories; Data-driven workflows; Wide-area data analytics; Large-scale science
Funding
- NSF [OCI1339036, OCI1441376, ACI1464317, ACI1640834]
- Ericsson
- Direct For Computer & Info Scie & Enginr
- Office of Advanced Cyberinfrastructure (OAC) [1464317] Funding Source: National Science Foundation
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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.
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