Improving Reproducibility of Data Science Pipelines through Transparent Provenance Capture
Summary: URSPRUNG integrates with execution environments to automatically capture static and runtime config, without code changes. It fuses system-level provenance with app-level signals (logs, stdout) via a DSL, achieving ~4% overhead. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Lukas Rupprecht (IBM)
- 2. James C. Davis (Virginia Tech)
- 3. Constantine Arnold (IBM)
- 4. Yaniv Gur (IBM)
- 5. Deepavali Bhagwat (IBM)
BibTeX Citation
@article{rupprecht_vldb20,
title = {{Improving Reproducibility of Data Science Pipelines through Transparent Provenance Capture}},
author = {Rupprecht, Lukas and Davis, James C. and Arnold, Constantine and Gur, Yaniv and Bhagwat, Deepavali},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {3354--3368},
doi = {10.14778/3415478.3415556},
url = {https://doi.org/10.14778/3415478.3415556},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,116 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD | 9.0047829e-05 |
| 8,609 | OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance From Database Query Event Logs | 2023 | VLDB | 5.2892055e-05 |
| 8,772 | A Study of Database Performance Sensitivity to Experiment Settings | 2022 | VLDB | 5.2683189e-05 |
| 11,203 | Unified Lineage System: Tracking Data Provenance at Scale | 2025 | SIGMOD | 4.9605343e-05 |
| 11,485 | ML-Asset Management: Curation, Discovery, and Utilization | 2025 | VLDB | 4.9605343e-05 |
| 11,592 | On the Feasibility and Benefits of Extensive Evaluation | 2024 | SIGMOD | 4.9605343e-05 |
| 12,007 | Flow Provenance in Temporal Interaction Networks | 2021 | SIGMOD | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 512 | Goods: Organizing Google's Datasets | 2016 | SIGMOD | 0.00017036842 |
| 1,506 | VisTrails: Visualization meets Data Management | 2006 | SIGMOD | 0.00010423054 |
| 1,562 | Titian: Data Provenance Support in Spark | 2016 | VLDB | 0.00010192248 |
| 1,776 | Putting Lipstick on Pig: Enabling Database-style Workflow Provenance | 2012 | VLDB | 9.6661217e-05 |
| 2,296 | Decibel: The Relational Dataset Branching System | 2016 | VLDB | 8.6729094e-05 |
| 2,448 | noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts | 2017 | VLDB | 8.4366124e-05 |
| 3,691 | Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML | 2020 | CIDR | 7.0771037e-05 |
| 3,732 | RAMP: A System for Capturing and Tracing Provenance in MapReduce Workflows | 2011 | VLDB | 7.0438962e-05 |
| 8,048 | Dependency-Driven Analytics: a Compass for Uncharted Data Oceans | 2017 | CIDR | 5.3842397e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,967 | Compact, Tamper-Resistant Archival of Fine-Grained Provenance | 2021 | VLDB |
| 2 | 949 | Provenance and Scientific Workflows: Challenges and Opportunities | 2008 | SIGMOD |
| 3 | 8,609 | OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance From Database Query Event Logs | 2023 | VLDB |
| 4 | 2,116 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD |
| 5 | 6,453 | Tracing Lineage Beyond Relational Operators | 2007 | VLDB |
| 6 | 13,645 | PROXAI: Interactive Provenance-Aware Debugging of Machine Learning Pipelines | 2026 | VLDB |
| 7 | 1,923 | Efficient Lineage Tracking For Scientific Workflows | 2008 | SIGMOD |
| 8 | 11,952 | DPDS: Assisting Data Science with Data Provenance | 2022 | VLDB |
| 9 | 12,209 | Ursprung: Provenance for Large-Scale Analytics Environments | 2019 | SIGMOD |
| 10 | 7,235 | Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science | 2021 | VLDB |