【摘要】 为了提高数据质量，数据修复技术得到了广泛的研究。数据修复通常使用约束，常用的有函数依赖、条件函数依赖和否定约束(Denial Constraints, DCs)等，其中，否定约束具有足够的表达能力，可以包含许多其他依赖项，因此在数据修复中得到了很好的应用。另外，数据通常是动态的，因此需要增量修复技术，以响应数据更新。该文提出一种满足否定约束的增量修复算法，结合辅助的索引结构，能有效地识别增量数据引起的DC冲突。大量的实验结果表明了该方法的有效性和高效性。更多还原

【Abstract】 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.更多还原