OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance From Database Query Event Logs
Summary: OneProvenance: a log-based system that efficiently extracts coarse-grained provenance from DB query event logs by recovering query execution dependencies via lightweight log analysis and novel event transformations. Adds filtering optimizations to cut noise and capture execution dependencies, yielding up to ~18× faster extraction than prior work and deployed at scale in Microsoft Purview. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Fotis Psallidas (Microsoft)
- 2. Ashvin Agrawal (Microsoft)
- 3. Chandru Sugunan (Snowflake)
- 4. Khaled Ibrahim (Microsoft)
- 5. Konstantinos Karanasos (Meta)
- 6. Jesús Camacho-Rodríguez (Microsoft)
- 7. Avrilia Floratou (Microsoft)
- 8. Carlo Curino (Microsoft)
- 9. Raghu Ramakrishnan (Microsoft)
BibTeX Citation
@article{psallidas_vldb23,
title = {{OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance From Database Query Event Logs}},
author = {Psallidas, Fotis and Agrawal, Ashvin and Sugunan, Chandru and Ibrahim, Khaled and Karanasos, Konstantinos and Camacho-Rodríguez, Jesús and Floratou, Avrilia and Curino, Carlo and Ramakrishnan, Raghu},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3662--3675},
doi = {10.14778/3611550.3611555},
url = {https://doi.org/10.14778/3611550.3611555},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,470 | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | 2023 | VLDB | 6.5833414e-05 |
| 10,838 | Toward Temporal Attribution Analytics in Dataflows | 2026 | VLDB | 4.9769913e-05 |
| 11,144 | Unified Lineage System: Tracking Data Provenance at Scale | 2025 | SIGMOD | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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