@incollection{fdi:010078830,
  title = {{D}ata quality checking for machine learning with {M}e{SQ}ua{L} [demonstration paper]},
  author = {{C}omignani, {U}. and {N}ovelli, {N}. and {B}erti-{E}quille, {L}aure},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his demo proposes {M}e{SQ}ua{L}, a system for profiling and check-ing data quality before further tasks, such as data analytics and machine learning. {M}e{SQ}ua{L} extends {SQL} for querying relational data with constraints on data quality and facilitates the verification of statistical tests. {T}he system includes: (1) a query interpreter for {SQ}ua{L}, 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 {SQ}ua{L} queries, and visualizing the results via data quality dashboards. {W}e 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.},
  keywords = {},
  booktitle = {{A}dvances in database technology : {EDBT} 2020},
  numero = {23},
  pages = {591--594},
  address = {{C}onstance},
  publisher = {{O}pen {P}roceedings},
  series = {{O}pen {P}roceedings},
  year = {2020},
  DOI = {10.5441/002/edbt.2020.71},
  ISBN = {978-3-89318-083-7},
  ISSN = {2367-2005},
  URL = {https://www.documentation.ird.fr/hor/fdi:010078830},
}