The F4U system for understanding the effects of data quality

Daniele Foroni, Matteo Lissandrini, Yannis Velegrakis

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)

Abstract

We demonstrate a system that enables a data-centric approach in understanding data quality. Instead of directly quantifying data quality as traditionally done, it disrupts the quality of the dataset and monitors the deviations in the output of an analytic task at hand. It computes the correlation factor between the disruption and the deviation and uses it as the quality metric. This allows users to understand not only the quality of their dataset but also the effect that present and future quality issues have to the intended analytic tasks. This is a novel data-centric approach aimed at complementing existing solutions. On top of the new information that it provides, and in contrast to existing techniques of data quality, it neither requires knowledge of the clean datasets, nor of the constraints on which the data should comply.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
Number of pages4
PublisherIEEE Computer Society Press
Publication dateApr 2021
Pages2717-2720
Article number9458779
ISBN (Print)978-1-7281-9185-0
ISBN (Electronic)978-1-7281-9184-3
DOIs
Publication statusPublished - Apr 2021
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: 19 Apr 202122 Apr 2021

Conference

Conference37th IEEE International Conference on Data Engineering, ICDE 2021
Country/TerritoryGreece
CityVirtual, Chania
Period19/04/202122/04/2021
SeriesProceedings of the International Conference on Data Engineering
ISSN1063-6382

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Data Cleaning
  • Data Mining
  • Data Profiling
  • Data Quality

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