Improving Data Quality: Consistency and Accuracy

Two central criteria for data quality are consistency and accuracy. Inconsistencies and errors in a database often emerge as violations of integrity constraints. Given a dirty database D, one needs automated methods to make it consistent, i.e., find a repair D that satisfies the constraints and "minimally" differs from D. Equally important is to ensure that the automatically-generated repair D is accurate, or makes sense, i.e., D differs from the "correct" data within a predefined bound. This paper studies effective methods for improving both data consistency and accuracy. We employ a class of conditional functional dependencies (CFDs) proposed in [6] to specify the consistency of the data, which are able to capture inconsistencies and errors beyond what their traditional counterparts can catch. To improve the consistency of the data, we propose two algorithms: one for automatically computing a repair D that satisfies a given set of CFDs, and the other for incremen...

• Type: Conference
• Year: 2007
• Where: VLDB
• Authors: Gao Cong, Wenfei Fan, Floris Geerts, Xibei Jia, Shuai Ma
• Keywords: Database, VLDB 2007