AN INCREMENTAL DATA REPAIR ALGORITHM SATISFYING NEGATION CONSTRAINTS

Data repairing techniques are extensively studied for improving data quality. Data repairing usually uses constraints, commonly-used constraints have functional dependencies, conditional functional dependencies and denial constraints (DCs), etc. where DCs are expressive enough to subsume many other dependencies, and are hence well employed in data repairing. In practice, data is typically dynamic, with this comes the need for incremental repairing techniques, in response to data updates. This paper presents the incremental repairing algorithm that satisfy denial constraints, combining auxiliary indexing structures such that DC violations incurred by incremental data can be efficiently identified. Extensive experiments show the effectiveness and efficiency of our algorithm.