Guided data repair

Mohamed Yakout, Ahmed Elmagarmid, Jennifer Neville, Mourad Ouzzani, Ihab F. Ilyas

Research output: Chapter in Book/Report/Conference proceedingChapter

124 Citations (Scopus)


In this paper we present GDR, a Guided Data Repair framework that incorporates user feedback in the cleaning process to enhance and accelerate existing automatic repair techniques while minimizing user involvement. GDR consults the user on the updates that are most likely to be benecial in improving data quality. GDR also uses machine learning methods to identify and apply the correct updates directly to the database without the actual involvement of the user on these specic updates. To rank potential updates for consultation by the user, we rst group these repairs and quantify the utility of each group using the decision-theory concept of value of information (VOI). We then apply active learning to order updates within a group based on their ability to improve the learned model. User feedback is used to repair the database and to adaptively rene the training set for the model. We empirically evaluate GDR on a real-world dataset and show signicant improvement in data quality using our user guided repairing process. We also, assess the trade-off between the user efforts and the resulting data quality.

Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
Number of pages11
Publication statusPublished - Feb 2011


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

Cite this

Yakout, M., Elmagarmid, A., Neville, J., Ouzzani, M., & Ilyas, I. F. (2011). Guided data repair. In Proceedings of the VLDB Endowment (5 ed., Vol. 4, pp. 279-289)