在PG约束下修复属性图
Repairing Property Graphs under PG-Constraints
AI总结：
本文提出在PG约束下修复属性图的方法，通过三种算法策略实现高效修复，实验表明标签删除可减少删除量，LP引导贪心算法运行效率更高。
摘要：
最近的图数据库标准化努力导致了像GQL和SQL/PGQ这样的标准查询语言以及属性图约束（PG-Constraints）等约束语言。在本文中，我们开始研究在PG约束下修复属性图。我们识别出PG约束的一个重要子集，编码拒绝约束，并包含递归作为关键特征，同时仍允许基于自动机的结构错误分析。我们提出了一个全面的修复管道来处理这些约束以修复属性图，涉及图拓扑的变化，并导致节点、边以及可选的标签删除。我们研究了三种修复过程的算法策略，基于整数线性规划（ILP）、一种简单的方法以及一种基于LP引导的贪心算法。我们的实验在各种现实世界数据集上揭示，使用标签删除修复可以比节点/边删除减少59%的删除。此外，基于LP引导的贪心算法相比ILP策略在运行时间上具有高达97%的优势，同时达到相同质量。
英文摘要：
Recent standardization efforts for graph databases lead to standard query languages like GQL and SQL/PGQ, and constraint languages like Property Graph Constraints (PG-Constraints). In this paper, we embark on the study of repairing property graphs under PG-Constraints. We identify a significant subset of PG-Constraints, encoding denial constraints and including recursion as a key feature, while still permitting automata-based structural analyses of errors. We present a comprehensive repair pipeline for these constraints to repair Property Graphs, involving changes in the graph topology and leading to node, edge and, optionally, label deletions. We investigate three algorithmic strategies for the repair procedure, based on Integer Linear Programming (ILP), a naive, and an LP-guided greedy algorithm. Our experiments on various real-world datasets reveal that repairing with label deletions can achieve a 59% reduction in deletions compared to node/edge deletions. Moreover, the LP-guided greedy algorithm offers a runtime advantage of up to 97% compared to the ILP strategy, while matching the same quality.
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