Although RDBs store vast amounts of rich, informative data spread across
interconnected tables, the progress of predictive machine learning models as
applied to such tasks arguably falls well behind advances in other domains such
as computer vision or natural language processing. This deficit stems, at least
in part, from the lack of established/public RDB benchmarks as needed for
training and evaluation purposes. As a result, related model development thus
far often defaults to tabular approaches trained on ubiquitous single-table
benchmarks, or on the relational side, graph-based alternatives such as GNNs
applied to a completely different set of graph datasets devoid of tabular
characteristics. To more precisely target RDBs lying at the nexus of these two
complementary regimes, we explore a broad class of baseline models predicated
on: (i) converting multi-table datasets into graphs using various strategies
equipped with efficient subsampling, while preserving tabular characteristics;
and (ii) trainable models with well-matched inductive biases that output
predictions based on these input subgraphs. Then, to address the dearth of
suitable public benchmarks and reduce siloed comparisons, we assemble a diverse
collection of (i) large-scale RDB datasets and (ii) coincident predictive
tasks. From a delivery standpoint, we operationalize the above four dimensions
(4D) of exploration within a unified, scalable open-source toolbox called
4DBInfer. We conclude by presenting evaluations using 4DBInfer, the results of
which highlight the importance of considering each such dimension in the design
of RDB predictive models, as well as the limitations of more naive approaches
such as simply joining adjacent tables. Our source code is released at
this https URL .

通过将多表数据集转化为图形并利用有效的子采样策略保留表格特性，以及输出基于这些输入子图的预测的具有相匹配归纳偏差的可训练模型，该研究试图解决关于关系型数据库 (RDBs) 的预测模型发展方面存在的问题，并提供了一个包含多尺度 RDB 数据集以及一致的预测任务的基准集，通过一个名为 4DBInfer 的统一可扩展开源工具箱来探索这些问题的 4 个方面。