Don't be SCAREd: use SCalable Automatic REpairing with maximal likelihood and bounded changes

By Mohamed Yakout, Laure Berti-Équille, Ahmed K. Elmagarmid. 2013

176 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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Citations in the corpus

AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, methodology): “Machine Learning in Data Cleaning: Machine learning has been widely used to improve the efficiency and/or reliability of data cleaning [19, 48, 49].”
BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “There are a number of other works that use machine learning to improve the efﬁciency and/or reliability of data cleaning [21,51,52].”
ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning (2016, methodology): “We will first describe ActiveClean and overview the entire framework, and a detailed description of the research challenges and algorithms can be found in [8].”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, methodology): “To evaluate data repairing on these datasets, we manually labeled 2,000 and 2,500 cells, respectively: We focused on tuples identiﬁed as erroneous by the error detection mechanisms of Holistic, KATARA, and SCARE.”
Towards Automated Data Cleaning Workflows (2019, background): “Other systems, such as SCARE [32] and ERACER [18] leverage probabilistic models to repair data and assume that all errors can be corrected without human involvement.”
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, methodology): “There are a number of other works that use machine learning to improve the efﬁciency and/or reliability of data cleaning [38, 37, 16].”
Interactive and Deterministic Data Cleaning (2016, methodology): “Given a set of user updates, they can be used as training data to train machine learning models, which in turn can be used to predict other repairs [41, 45].”
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing (2015, methodology): “The precision/recall of Katara , EQ and SCARE on RelationalTables , are reported in Table 6.”
Data Cleaning: Overview and Emerging Challenges (2016, background): “Finally, Yakout et al. [81] employ Machine Learning to improve the reliability of data cleaning.”
ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “Data Cleaning: There are a number of other works that use machine learning to improve the efficiency and/or reliability of data cleaning [16,35,36].”
Combining Quantitative and Logical Data Cleaning (2015, methodology): “We propose instead to pick a repair that minimizes statistical distortion with respect to an ideal relation [10, 3, 26].”