Query-Oriented Data Cleaning with Oracles

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AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, background): “A large body of literature addresses relational queries over databases with errors by focusing on specific classes of queries [5], leveraging constraints over the input relation [10], integration with crowdsourcing [8].”
BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “Bergman et al. [10] proposed identifying errors in selection query results and generating crowd-scoured queries to determine ﬁxes to the base data.”
Data Cleaning: Overview and Emerging Challenges (2016, background): “We will also brieﬂy describe the related ﬁelds of query-driven cleaning and consistent query answering [9, 7, 4] as these works have studied the problem of how inconsistencies affect individual queries.”
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, background): “Similarly, Bergman et al. explore the problem of query-oriented data cleaning [7], where given a query, they clean data relevant to that query.”
ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “explore the problem of query-oriented data cleaning [6], where given a query, they clean data relevant to that query.”