%0 Book Section
%9 OS CH : Chapitres d'ouvrages scientifiques
%A Comignani, U.
%A Novelli, N.
%A Berti-Equille, Laure
%T Data quality checking for machine learning with MeSQuaL [demonstration paper]
%B Advances in database technology : EDBT 2020
%C Constance
%D 2020
%E Bonifati, A.
%E Zhou, I.
%E Vaz Salles, M. A.
%E Böhm, A.
%E Olteanu, D.
%E Fletcher, G.
%E Khan, A.
%E Yang, B.
%L fdi:010078830
%G ENG
%I Open Proceedings
%@ 978-3-89318-083-7
%N 23
%P 591-594
%R 10.5441/002/edbt.2020.71
%U https://www.documentation.ird.fr/hor/fdi:010078830
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers20-07/010078830.pdf
%W Horizon (IRD)
%X This demo proposes MeSQuaL, a system for profiling and check-ing 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 showcaseour system in action with various scenarios on real-world datasets and show its usability for monitoring data quality over timeand checking the quality of data on-demand.
%S Open Proceedings
%B International Conference on Extending Database Technology
%8 2020/30/03-2020/04/02
%$ 122