Data Cleaning in the Era of Data Science: Challenges and Opportunities
Summary: Traditional one-shot, monolithic cleaning tools fail for iterative, multi-pipeline data-science workflows with many operators. Paper pinpoints pipeline diversity, tool monoethnicity, and subjective/ad-hoc errors, calling for composable, pipeline-aware cleaning primitives. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,684 | Dagger: A Data (not code) Debugger | 2020 | CIDR | 5.3720749e-05 |
| 9,306 | Debugging Large-Scale Data Science Pipelines using Dagger | 2020 | VLDB | 4.3572942e-05 |
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