Leveraging Approximate Constraints for Localized Data Error Detection
Mohan Zhang, Oliver Schulte, Yudong Luo · 2021
Error detection is key for data quality management. AI techniques can leverage user domain knowledge to identifying sets of erroneous records that conflict with domain knowledge. To represent a wide range of user domain knowledge, several recent papers have developed and utilized soft approximate constraints (ACs) that a data relation is expected to satisfy only to a certain degree, rather than completely. We introduce error localization, a new AI-based technique for enhancing error detection with ACs.