AI 中文总结
研究动态数据集中增量拒绝约束发现问题，提出EviDC算法，将现有DCs组织成DCTrie结构，沿可达违规路径扩展证据并修剪无关分支，在真实和合成数据集上评估，该算法能减少中间证据、提高运行效率，具有有效性和可扩展性。
AI 中文摘要
拒绝约束（DCs）是一类重要的完整性约束，在数据质量管理中广泛应用。在动态数据集中，新插入元组可能使现有DCs无效，需更新约束集。现有增量DC发现方法在构建证据时未利用现有DCs的结构信息，仍会生成大量中间证据。我们提出EviDC，一种违规引导的增量DC发现算法。EviDC将现有DCs组织成名为DCTrie的前缀树结构，从根到叶的每条路径代表潜在违规路径。增量处理时，证据仅沿可达违规路径扩展，尽早修剪无关分支。我们在真实和合成数据集上评估EviDC。结果表明，EviDC在多数场景下减少了中间证据并提高了运行时效率。随着插入率和数据集大小增加，性能提升更显著，显示了违规引导证据构建的有效性和可扩展性。
英文摘要
Denial Constraints (DCs) are an important class of integrity constraints and have been widely used in data quality management. In dynamic datasets, newly inserted tuples may invalidate existing DCs and require the constraint set to be updated. Existing incremental DC discovery methods still generate a large amount of intermediate evidence because they do not exploit the structural information of existing DCs during evidence construction. We propose EviDC, a violation-guided incremental DC discovery algorithm. EviDC organizes existing DCs into a prefix tree structure called DCTrie, in which each path from the root to a leaf represents a potential violation path. During incremental processing, evidence is expanded only along reachable violation paths, while irrelevant branches are pruned as early as possible. We evaluate EviDC on real-world and synthetic datasets. The results show that EviDC reduces intermediate evidence and improves runtime efficiency in most scenarios. The performance gain becomes more pronounced as the insertion ratio and dataset size increase, showing the effectiveness and scalability of violation-guided evidence construction.
