Ursprung: Provenance for Large-Scale Analytics Environments
Summary: Ursprung collects minimal system-level provenance to map data–process relationships in large analytics. It enables domain-specific provenance via capture rules and uses event hierarchies to synthesize provenance into compact summaries, reducing storage and speeding queries. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Lukas Rupprecht (IBM)
- 2. James C. Davis (IBM; Virginia Polytechnic Institute and State University)
- 3. Constantine Arnold (IBM)
- 4. Alexander Lubbock (Vanderbilt University)
- 5. Darren Tyson (Vanderbilt University)
- 6. Deepavali Bhagwat (IBM)
BibTeX Citation
@inproceedings{rupprecht_sigmod19,
title = {{Ursprung: Provenance for Large-Scale Analytics Environments}},
author = {Rupprecht, Lukas and Davis, James C. and Arnold, Constantine and Lubbock, Alexander and Tyson, Darren and Bhagwat, Deepavali},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3320235},
url = {https://dl.acm.org/doi/10.1145/3299869.3320235},
year = {2019}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 514 | Goods: Organizing Google's Datasets | 2016 | SIGMOD | 0.00017178673 |
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