  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.
