DQDF: Data-Quality-Aware Dataframes
Summary: DQDF embeds data-quality checks directly into Python dataframes, removing separate QC state maintenance. Automatic metadata-change detection and per-check context reuse accelerate QC on evolving data, delivering 40–80% faster quality evaluation with <10% memory overhead. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Phanwadee Sinthong (University of California Irvine)
- 2. Dhaval Patel (IBM)
- 3. Nianjun Zhou (IBM)
- 4. Shrey Shrivastava (IBM)
- 5. Arun Iyengar (IBM)
- 6. Anuradha Bhamidipaty (IBM)
BibTeX Citation
@article{sinthong_vldb22,
title = {{DQDF: Data-Quality-Aware Dataframes}},
author = {Sinthong, Phanwadee and Patel, Dhaval and Zhou, Nianjun and Shrivastava, Shrey and Iyengar, Arun and Bhamidipaty, Anuradha},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {4},
pages = {949--957},
doi = {10.14778/3503585.3503602},
url = {https://doi.org/10.14778/3503585.3503602},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,442 | T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,308 | Automating Large-Scale Data Quality Verification | 2018 | VLDB | 0.0001107886 |
| 1,369 | Towards Scalable Dataframe Systems | 2020 | VLDB | 0.00010899832 |
| 3,455 | Scaling Spark in the Real World: Performance and Usability | 2015 | VLDB | 7.2884813e-05 |
| 3,706 | TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines | 2020 | SIGMOD | 7.0829596e-05 |
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