CURED: Creating, Understanding, and Repairing Errors Demonstrator

Abstract:Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications. Recent research at the intersection of Machine Learning (ML) and Database Management Systems (DBMS) highlights the potential of statistical learning algorithms for error detection and cleaning. This paper combines our recent work on ML-based data cleaning and error models in a unified demonstrator. The web application allows users to upload tabular data, perturb the data with realistic data dependent errors and use modern ML methods to clean and understand error mechanisms in data. Our demonstrator helps to bridge the gap between theoretical advancements and intuitive practical insights in the context of error models and data cleaning algorithms for tabular data. The demonstrator is available at this https URL

Machine Learning (cs.LG)

arXiv:2607.20140 [cs.LG]

(or arXiv:2607.20140v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.20140 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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