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
- 2. Dhaval Patel
- 3. Nianjun Zhou
- 4. Shrey Shrivastava
- 5. Arun Iyengar
- 6. Anuradha Bhamidipaty
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,871 | T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data | 2025 | VLDB | 4.1905499e-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 |
|---|---|---|---|---|
| 1,427 | Towards Scalable Dataframe Systems | 2020 | VLDB | 0.00012033503 |
| 1,481 | Automating Large-Scale Data Quality Verification | 2018 | VLDB | 0.00011715754 |
| 3,495 | TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines | 2020 | SIGMOD | 7.0383507e-05 |
| 3,536 | Scaling Spark in the Real World: Performance and Usability | 2015 | VLDB | 6.9938207e-05 |
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| 733 | Discovering Data Quality Rules | 2008 | VLDB | 0.00017428132 |
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