Abstract
Scientific data collections grow ever larger, both in terms of the size of individual data items and of the number and complexity of items. To use and manage them, it is important to directly address issues of robust and actionable provenance. We identify three key drivers as our focus: managing the size and complexity of metadata, lack of a priori information to match usage intents between publishers and consumers of data, and support for campaigns over collections of data driven by multi-disciplinary, collaborating teams. We introduce the Hoarde abstraction as an attempt to formalize a way of looking at collections of data to make them more tractable for later use. Hoarde leverages middleware and systems infrastructures for scientific and technical data management. Through the lens of a select group of challenging data usage scenarios, we discuss some of the aspects of implementation, usage, and forward portability of this new view on data management.
Original language | English |
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Title of host publication | Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1806-1817 |
Number of pages | 12 |
ISBN (Electronic) | 9781728125190 |
DOIs | |
State | Published - Jul 2019 |
Event | 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 - Richardson, United States Duration: Jul 7 2019 → Jul 9 2019 |
Publication series
Name | Proceedings - International Conference on Distributed Computing Systems |
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Volume | 2019-July |
Conference
Conference | 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 |
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Country/Territory | United States |
City | Richardson |
Period | 07/7/19 → 07/9/19 |
Funding
Without the continued support from the Department of Energy’s Office of Advanced Scientific Computing Research, the projects upon which this future vision rests, including SIRIUS, MONA, and SENSEI, would not be possible. Additionally, support from the DOE computing facilities in Oak Ridge and NERSC, as well as the National Science Foundation, was also critical. This work was also supported in part by 1U24CA180924-01A1, 3U24CA215109-02, and 1UG3CA225021-01 from the National Cancer Institute, R01LM011119-01 and R01LM009239 from the U.S. National Library of Medicine.
Keywords
- Data provenance
- Metadata management
- Reproducibility
- Scientific data management