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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 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.
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Facilitating Data Discovery for Large-scale Science Facilities using Knowledge Networks
Large-scale multiuser scientific facilities, such as geographically distributed observatories, remote instruments, and experimental platforms, represent some of the largest national investments and can enable dramatic advances across many areas of science. Recent examples of such advances include the detection of gravitational waves and the imaging of a black hole’s event horizon. However, as the number of such facilities and their users grow, along with the complexity, diversity, and volumes of their data products, finding and accessing relevant data is becoming increasingly challenging, limiting the potential impact of facilities. These challenges are further amplified as scientists and application workflows increasingly try to integrate facilities’ data from diverse domains. In this paper, we leverage concepts underlying recommender systems, which are extremely effective in e-commerce, to address these data-discovery and data-access challenges for large-scale distributed scientific facilities. We first analyze data from facilities and identify and model user-query patterns in terms of facility location and spatial localities, domain-specific data models, and user associations. We then use this analysis to generate a knowledge graph and develop the collaborative knowledge-aware graph attention network (CKAT) recommendation model, which leverages graph neural networks (GNNs) to explicitly encode the collaborative signals through propagation and combine them with knowledge associations. Moreover, we integrate a knowledge-aware neural attention mechanism to enable the CKAT to pay more attention to key information while reducing irrelevant noise, thereby increasing the accuracy of the recommendations. We apply the proposed model on two real-world facility datasets and empirically demonstrate that the CKAT can effectively facilitate data discovery, significantly outperforming several compelling state-of-the-art baseline models.
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- PAR ID:
- 10285112
- Date Published:
- Journal Name:
- 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
- Page Range / eLocation ID:
- 651 to 660
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPS49936.2021.00073
