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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
11396
Venue
VLDB
Year
2017
Pagerank
4.4039656e-05
Overall Rank
9,056 | 37.00%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,350 Northstar: An Interactive Data Science System 2018 VLDB 0.00012431059
11,454 Contextual Data Cleaning with Ontology FDs 2021 SIGMOD 4.1945683e-05
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Showing 14 of 14 cited papers.

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

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