AJAX: an extensible data cleaning tool
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Machine Learning
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
A cost-based model and effective heuristic for repairing constraints by value modification
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
ALIAS: an active learning led interactive deduplication system
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Improving data quality: consistency and accuracy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Conditional functional dependencies for capturing data inconsistencies
ACM Transactions on Database Systems (TODS)
Semandaq: a data quality system based on conditional functional dependencies
Proceedings of the VLDB Endowment
Estimating the confidence of conditional functional dependencies
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Proceedings of the VLDB Endowment
Interaction between record matching and data repairing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Statistical distortion: consequences of data cleaning
Proceedings of the VLDB Endowment
The data analytics group at the qatar computing research institute
ACM SIGMOD Record
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Improving data quality is a time-consuming, labor-intensive and often domain specific operation. Existing data repair approaches are either fully automated or not efficient in interactively involving the users. We present a demo of GDR, a Guided Data Repair system that uses a novel approach to efficiently involve the user alongside automatic data repair techniques to reach better data quality as quickly as possible. Specifically, GDR generates data repairs and acquire feedback on them that would be most beneficial in improving the data quality. GDR quantifies the data quality benefit of generated repairs by combining mechanisms from decision theory and active learning. Based on these benefit scores, groups of repairs are ranked and displayed to the user. User feedback is used to train a machine learning component to eventually replace the user in deciding on the validity of a suggested repair. We describe how the generated repairs are ranked and displayed to the user in a "useful-looking" way and demonstrate how data quality can be effectively improved with minimal feedback from the user.