Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases

TL;DR

This paper introduces SRP, a unified framework that enhances predictive modeling on relational databases by capturing both unary and composite dependencies through synthesis, retrieval, and propagation mechanisms.

Contribution

SRP is the first framework to fully exploit both unary and composite dependencies in relational databases for improved predictive modeling.

Findings

SRP outperforms existing methods on five real-world datasets.

The retrieval mechanism significantly improves model receptive field.

Extensive analysis provides practical guidelines for application.

Abstract

Relational databases (RDBs) have become the industry standard for storing massive and heterogeneous data. However, despite the widespread use of RDBs across various fields, the inherent structure of relational databases hinders their ability to benefit from flourishing deep learning methods. Previous research has primarily focused on exploiting the unary dependency among multiple tables in a relational database using the primary key - foreign key relationships, either joining multiple tables into a single table or constructing a graph among them, which leaves the implicit composite relations among different tables and a substantial potential of improvement for predictive modeling unexplored. In this paper, we propose SRP, a unified predictive modeling framework that synthesizes features using the unary dependency, retrieves related information to capture the composite dependency, and propagates messages across a constructed graph to learn adjacent patterns for prediction on relation databases. By introducing a new retrieval mechanism into RDB, SRP is designed to fully capture both the unary and the composite dependencies within a relational database, thereby enhancing the receptive field of tabular data prediction. In addition, we conduct a comprehensive analysis on the components of SRP, offering a nuanced understanding of model behaviors and practical guidelines for future applications. Extensive experiments on five real-world datasets demonstrate the effectiveness of SRP and its potential applicability in industrial scenarios. The code is released at https://github.com/NingLi670/SRP.