@inproceedings{fdi:010085547,
  title = {2nd international workshop on data quality assessment for machine learning [r{\'e}sum{\'e}]},
  author = {{P}atel, {H}. and {I}shikawa, {F}. and {B}erti-{E}quille, {L}aure and {G}upta, {N}. and {M}ehta, {S}. and {M}asuda, {S}. and {M}ujumdar, {S}. and {A}fzal, {S}. and {B}edathur, {S}. and {N}ishi, {Y}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he 2nd {I}nternational {W}orkshop on {D}ata {Q}uality {A}ssessment for {M}achine {L}earning ({DQAML}'21) is organized in conjunction with the {S}pecial {I}nterest {G}roup on {K}nowledge {D}iscovery and {D}ata {M}ining ({SIGKDD}). {T}his workshop aims to serve as a forum for the presentation of research related to data quality assessment and remediation in {AI}/{ML} pipeline. {D}ata quality is a critical issue in the data preparation phase and involves numerous challenging problems related to detection, remediation, visualization and evaluation of data issues. {T}he workshop aims to provide a platformto researchers and practitioners to discuss such challenges across different modalities of data like structured, time series, text and graphical. {T}he aim is to attract perspectives from both industrial and academic circles.},
  keywords = {},
  numero = {},
  pages = {4147--4148},
  booktitle = {{KDD}'21 : proceedings of the 27th {ACM} {SIGKDD} conference on knowledge discovery and data mining},
  year = {2021},
  DOI = {10.1145/3447548.3469468},
  ISBN = {978-1-4503-8332-5},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085547},
}