2nd International Workshop on Data Quality Assessment for Machine Learning

Hima Patel, Fuyuki Ishikawa, Laure Berti-Equille, Nitin Gupta, Sameep Mehta, Satoshi Masuda, Shashank Mujumdar, Shazia Afzal, Srikanta Bedathur, Yasuharu Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The 2nd International Workshop on Data Quality Assessment for Machine Learning (DQAML'21) is organized in conjunction with the Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). This workshop aims to serve as a forum for the presentation of research related to data quality assessment and remediation in AI/ML pipeline. Data 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. The workshop aims to provide a platform to researchers and practitioners to discuss such challenges across different modalities of data like structured, time series, text and graphical. The aim is to attract perspectives from both industrial and academic circles.

Original languageEnglish
Title of host publicationKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages4147-4148
Number of pages2
ISBN (Electronic)9781450383325
DOIs
Publication statusPublished - 14 Aug 2021
Externally publishedYes
Event27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Duration: 14 Aug 202118 Aug 2021

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/TerritorySingapore
CityVirtual, Online
Period14/08/2118/08/21

Keywords

  • data assessment
  • data quality
  • machine learning

Fingerprint

Dive into the research topics of '2nd International Workshop on Data Quality Assessment for Machine Learning'. Together they form a unique fingerprint.

Cite this