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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)

Paper ID
12964
Venue
VLDB
Year
2022
Pagerank
4.427232e-05
Overall Rank
8,915 | 37.99%
DOI
10.14778/3503585.3503602

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,867 T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data 2025 VLDB 4.1945683e-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 0.0001204248
1,482 Automating Large-Scale Data Quality Verification 2018 VLDB 0.00011725533
3,491 TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines 2020 SIGMOD 7.0451276e-05
3,535 Scaling Spark in the Real World: Performance and Usability 2015 VLDB 6.9992495e-05
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