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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. El Kindi Rezig (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{rezig_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{Data Cleaning in the Era of Data Science: Challenges and Opportunities}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Rezig, El Kindi},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 5,404 | Dagger: A Data (not code) Debugger | 2020 | CIDR | 6.2294192e-05 |
| 9,495 | Debugging Large-Scale Data Science Pipelines using Dagger | 2020 | VLDB | 5.2616335e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 7,179 | CleanM: An Optimizable Query Language for Unified Scale-Out Data Cleaning | 2017 | VLDB |
| 2 | 3,580 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB |
| 3 | 4,890 | Descriptive and Prescriptive Data Cleaning | 2014 | SIGMOD |
| 4 | 5,835 | Semi-Supervised Data Cleaning with Raha and Baran | 2021 | CIDR |
| 5 | 11,713 | From Papers to Practice: The openclean Open-Source Data Cleaning Library | 2021 | VLDB |
| 6 | 201 | Declarative Data Cleaning: Language, Model, and Algorithms | 2001 | VLDB |
| 7 | 6,392 | Qualitative Data Cleaning | 2016 | VLDB |
| 8 | 7,232 | Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications | 2023 | SIGMOD |
| 9 | 1,351 | Detecting Data Errors: Where are we and what needs to be done? | 2016 | VLDB |
| 10 | 1,323 | Data Cleaning: Overview and Emerging Challenges | 2016 | SIGMOD |