ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning

Summary: ActiveClean is a progressive data-cleaning framework that interleaves cleaning with ML training, updating models as analysts clean small data batches. Key ideas include importance weighting, dirty-data detection, and a visual interface, enabling robust learning in high-dimensional pipelines, demonstrated on video classification and topic modeling. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h6a1fa34e3e3b2912
Venue: SIGMOD
Year: 2016
Pagerank: 5.9588636e-05
Overall Rank: 5,881 | 60.47%
DOI: 10.1145/2882903.2899409

Incoming Non-self Citations Over Time

Authors

1. Sanjay Krishnan (University of California Berkeley)
2. Michael J. Franklin (University of California Berkeley)
3. Ken Goldberg (University of California Berkeley)
4. Jiannan Wang (Simon Fraser University)
5. Eugene Wu (Columbia University)

BibTeX Citation

@inproceedings{krishnan_sigmod16,
        title = {{ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning}},
        author = {Krishnan, Sanjay and Franklin, Michael J. and Goldberg, Ken and Wang, Jiannan and Wu, Eugene},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2899409},
        url = {https://dl.acm.org/doi/10.1145/2882903.2899409},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
483	ActiveClean: Interactive Data Cleaning For Statistical Modeling	2016	VLDB	0.00017590977
1,308	Automating Large-Scale Data Quality Verification	2018	VLDB	0.0001107886
2,371	SCODED: Statistical Constraint Oriented Data Error Detection	2020	SIGMOD	8.5615698e-05
5,100	Towards Benchmarking Feature Type Inference for AutoML Platforms	2021	SIGMOD	6.27349e-05
5,954	Semi-Supervised Data Cleaning with Raha and Baran	2021	CIDR	5.9357922e-05
7,068	How do Categorical Duplicates Affect ML? A New Benchmark and Empirical Analyses	2024	VLDB	5.6083188e-05

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
433	Corleone: Hands-Off Crowdsourcing for Entity Matching	2014	SIGMOD	0.00018332741
652	Don’t be SCAREd: Use SCalable Automatic REpairing with Maximal Likelihood and Bounded Changes	2013	SIGMOD	0.00015121325
716	Guided Data Repair	2011	VLDB	0.00014553463
1,720	A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data	2014	SIGMOD	9.7965659e-05
8,777	Wisteria: Nurturing Scalable Data Cleaning Infrastructure	2015	VLDB	5.2792451e-05
8,870	Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views	2015	VLDB	5.2601766e-05

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