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Communication dans un congrès

Data Quality Checking for Machine Learning with MeSQuaL

Abstract : This demo proposes MeSQuaL, a system for profiling and checking data quality before further tasks, such as data analytics and machine learning. MeSQuaL extends SQL for querying relational data with constraints on data quality and facilitates the verification of statistical tests. The system includes: (1) a query interpreter for SQuaL, the SQL-extended language we propose for declaring and querying data with data quality checks and statistical tests; (2) an extensible library of user-defined functions for profiling the data and computing various data quality indicators;and (3) a user interface for declaring data quality constraints,profiling data, monitoring data quality with SQuaL queries, and visualizing the results via data quality dashboards. We showcase our system in action with various scenarios on real-world datasets and show its usability for monitoring data quality over time and checking the quality of data on-demand
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https://hal.archives-ouvertes.fr/hal-02865824
Contributeur : Laure Berti-Equille <>
Soumis le : vendredi 12 juin 2020 - 08:53:47
Dernière modification le : mardi 21 juillet 2020 - 15:54:59

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Ugo Comignani, Noël Novelli, Laure Berti-Équille. Data Quality Checking for Machine Learning with MeSQuaL. Advances in Database Technology - EDBT 2020, 23rd International Conference on Extending Database Technology,, Mar 2020, Copenhagen, Denmark. ⟨hal-02865824⟩

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