TY - GEN
T1 - Supporting data-driven workflows enabled by large scale observatories
AU - Zamani, Ali Reza
AU - Abdelbaky, Moustafa
AU - Balouek-Thomert, Daniel
AU - Rodero, Ivan
AU - Parashar, Manish
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - 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.
AB - 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.
KW - Data-driven workflows
KW - Large scale observatories
KW - Large-scale science
KW - Wide-area data analytics
UR - http://www.scopus.com/inward/record.url?scp=85043772204&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043772204&partnerID=8YFLogxK
U2 - 10.1109/eScience.2017.95
DO - 10.1109/eScience.2017.95
M3 - Conference contribution
AN - SCOPUS:85043772204
T3 - Proceedings - 13th IEEE International Conference on eScience, eScience 2017
SP - 592
EP - 595
BT - Proceedings - 13th IEEE International Conference on eScience, eScience 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on eScience, eScience 2017
Y2 - 24 October 2017 through 27 October 2017
ER -