A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets

Summary: Proposes Data Quality Metric (DQM) to quantify undetected errors after crowd-cleaning. Introduces FP/FN-resistant species estimators for distinct remaining errors under incomplete gold standards, with faster convergence across three real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: hb0765b4b59a91afd
Venue: VLDB
Year: 2017
Pagerank: 5.1868213e-05
Overall Rank: 9,385 | 36.91%
DOI: 10.14778/3115404.3115414

Incoming Non-self Citations Over Time

Authors

1. Yeounoh Chung (Brown University)
2. Sanjay Krishnan (University of California Berkeley)
3. Tim Kraska (Brown University)

BibTeX Citation

@article{chung_vldb17,
        title = {{A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets}},
        author = {Chung, Yeounoh and Krishnan, Sanjay and Kraska, Tim},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {11},
        pages = {1094},
        doi = {10.14778/3115404.3115414},
        url = {https://doi.org/10.14778/3115404.3115414},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,409	Northstar: An Interactive Data Science System	2018	VLDB	0.00010743451
11,959	Contextual Data Cleaning with Ontology FDs	2021	SIGMOD	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
77	Sampling-Based Estimation of the Number of Distinct Values of an Attribute	1995	VLDB	0.00036828234
92	CrowdDB: Answering Queries with Crowdsourcing	2011	SIGMOD	0.00034672523
188	A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification	2005	SIGMOD	0.00025872962
198	CrowdER: Crowdsourcing Entity Resolution	2012	VLDB	0.00025555196
433	Corleone: Hands-Off Crowdsourcing for Entity Matching	2014	SIGMOD	0.00018332741
526	Improving Data Quality: Consistency and Accuracy	2007	VLDB	0.00016886621
623	Entity Resolution: Theory, Practice & Open Challenges	2012	VLDB	0.00015483844
716	Guided Data Repair	2011	VLDB	0.00014553463
1,043	Data Cleaning: Overview and Emerging Challenges	2016	SIGMOD	0.00012335114
1,344	Detecting Data Errors: Where are we and what needs to be done?	2016	VLDB	0.00010956518
1,720	A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data	2014	SIGMOD	9.7965659e-05
2,645	Progressive Approach to Relational Entity Resolution	2014	VLDB	8.177221e-05
3,812	Incremental Record Linkage	2014	VLDB	7.0085181e-05
6,880	Estimating the Impact of Unknown Unknowns on Aggregate Query Results	2016	SIGMOD	5.6573214e-05

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