Relational databases are extensively utilized in a variety of modern information system applications, and they always carry valuable data patterns. There are a huge number of data mining or machine learning tasks conducted on relational databases. However, it is worth noting that there are limited machine learning models specifically designed for relational databases, as most models are primarily tailored for single table settings. Consequently, the prevalent approach for training machine learning models on data stored in relational databases involves performing feature engineering to merge the data from multiple tables into a single table and subsequently applying single table models. This approach not only requires significant effort in feature engineering but also destroys the inherent relational structure present in the data. To address these challenges, we propose a novel framework called Graph-based Feature Synthesis (GFS). GFS formulates the relational database as a heterogeneous graph, thereby preserving the relational structure within the data. By leveraging the inductive bias from single table models, GFS effectively captures the intricate relationships inherent in each table. Additionally, the whole framework eliminates the need for manual feature engineering. In the extensive experiment over four real-world multi-table relational databases, GFS outperforms previous methods designed for relational databases, demonstrating its superior performance.

翻译：关系数据库广泛应用于现代信息系统应用中，始终承载着有价值的数据模式。大量数据挖掘或机器学习任务基于关系数据库展开。然而，值得注意的是，专门为关系数据库设计的机器学习模型十分有限，因为大多数模型主要针对单表场景定制。因此，对存储在关系数据库中的数据进行机器学习模型训练的常见方法，是通过特征工程将多表数据合并为单表结构，再应用单表模型。这种方法不仅在特征工程中耗费大量精力，还破坏了数据中固有的关系结构。为解决这些挑战，我们提出了一种称为基于图的特征合成（GFS）的新型框架。GFS将关系数据库构建为异构图，从而保留数据中的关系结构。通过利用单表模型的归纳偏置，GFS有效捕获每个表中固有的复杂关系。此外，该框架完全消除了手动特征工程的需求。在四个真实多表关系数据库的广泛实验中，GFS的性能优于先前针对关系数据库设计的方法，展现出其卓越的表现力。