Primary keys (PKs) and foreign keys (FKs) are important elements of relational schemata in various applications, such as query optimization and data integration. However, in many cases, these constraints are unknown or not documented. Detecting them manually is time-consuming and even infeasible in large-scale datasets. We study the problem of discovering primary keys and foreign keys automatically and propose an algorithm to detect both, namely Ho listic P rimary Key and F oreign Key Detection (HoPF). PKs and FKs are subsets of the sets of unique column combinations (UCCs) and inclusion dependencies (INDs), respectively, for which efficient discovery algorithms are known. Using score functions, our approach is able to effectively extract the true PKs and FKs from the vast sets of valid UCCs and INDs. Several pruning rules are employed to speed up the procedure. We evaluate precision and recall on three benchmarks and two real-world datasets. The results show that our method is able to retrieve on average 88% of all primary keys, and 91% of all foreign keys. We compare the performance of HoPF with two baseline approaches that both assume the existence of primary keys.

中文翻译：

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整体主键和外键检测

主键 (PK) 和外键 (FK) 是各种应用程序中关系模式的重要元素，例如查询优化和数据集成。然而，在许多情况下，这些约束是未知的或没有记录。手动检测它们非常耗时，在大规模数据集中甚至不可行。我们研究了自动发现主键和外键的问题，并提出了一种检测两者的算法，即Ho listic Primary Key和Foreign Key Detection（HoPF）。PK 和 FK 分别是唯一列组合 (UCC) 和包含依赖项 (IND) 集的子集，已知有效的发现算法。使用评分函数，我们的方法能够有效地从大量有效的 UCC 和 IND 中提取真实的 PK 和 FK。几个修剪规则被用来加速这个过程。我们在三个基准和两个真实世界的数据集上评估精度和召回率。结果表明，我们的方法平均能够检索到所有主键的 88% 和所有外键的 91%。我们将 HoPF 的性能与两种都假设主键存在的基线方法进行比较。