Truth $\neq$ Frequency: Leveraging Dependencies for Subset Repair

Inconsistent values are commonly encountered in real-world applications, which can negatively impact data analysis and decision-making. While existing research primarily focuses on identifying the smallest removal set to resolve inconsistencies, recent studies have shown that the minimum repairing principle is often incorrect as it ignores the signals provided by the attribute values. Some approaches use the most frequent values as guidance for the subset repair. This strategy has been criticized for its potential to inaccurately identify errors. To address these issues, we consider the dependencies between attribute values to determine a more appropriate subset repair. Our main contributions include (1) formalizing the optimal subset repair problem with attribute dependencies and analyzing its computational hardness; (2) computing the exact solution using integer linear programming; (3) developing an approximate algorithm with performance guarantees based on cliques and LP relaxation; and (4) designing a probabilistic approach with an approximation bound for efficiency. Experimental results on realworld datasets validate the effectiveness of our methods in both subset repair performance and downstream applications.