Data Cleaning publication categorizer

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.