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Title: Enabling Data Streaming-based Science Gateways through Federated Cyberinfrastructure
Large scientific facilities are unique and complex infrastructures that have become fundamental instruments for enabling high quality, world-leading research to tackle scientific problems at unprecedented scales. Cyberinfrastructure (CI) is an essential component of these facilities, providing the user community with access to data, data products, and services with the potential to transform data into knowledge. However, the timely evolution of the CI available at large facilities is challenging and can result in science communities requirements not being fully satisfied. Furthermore, integrating CI across multiple facilities as part of a scientific workflow is hard, resulting in data silos. In this paper, we explore how science gateways can provide improved user experiences and services that may not be offered at large facility datacenters. Using a science gateway supported by the Science Gateway Community Institute, which provides subscription-based delivery of streamed data and data products from the NSF Ocean Observatories Initiative (OOI), we propose a system that enables streaming-based capabilities and workflows using data from large facilities, such as the OOI, in a scalable manner. We leverage data infrastructure building blocks, such as the Virtual Data Collaboratory, which provides data and comput- ing capabilities in the continuum to efficiently and collaboratively integrate multiple data-centric more » CIs, build data-driven workflows, and connect large facilities data sources with NSF-funded CI, such as XSEDE. We also introduce architectural solutions for running these workflows using dynamically provisioned federated CI. « less
Authors:
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Award ID(s):
1640834 1835692 1745246 1826997
Publication Date:
NSF-PAR ID:
10187419
Journal Name:
Gateways 2019
Sponsoring Org:
National Science Foundation
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Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. 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