DPDS: Assisting Data Science with Data Provenance
Summary: DPDS embeds fine-grained, per-dataframe-element provenance capture into Python/pandas pipelines via an unobtrusive observer pattern. A Neo4j-backed provenance graph and UI expose how each transformation changes data, supporting defensible cleaning and model preparation. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Adriane Chapman (University of Southampton)
- 2. Luca Lauro (Roma Tre University)
- 3. Paolo Missier (Newcastle University)
- 4. Riccardo Torlone (Roma Tre University)
BibTeX Citation
@article{chapman_vldb22,
title = {{DPDS: Assisting Data Science with Data Provenance}},
author = {Chapman, Adriane and Lauro, Luca and Missier, Paolo and Torlone, Riccardo},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3614--3617},
doi = {10.14778/3554821.3554857},
url = {https://doi.org/10.14778/3554821.3554857},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,431 | Towards Scalable Dataframe Systems | 2020 | VLDB | 0.00010807221 |
| 8,050 | Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science | 2021 | VLDB | 5.5000099e-05 |
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