NeurIPS2024
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs
Minjie Wang, Quan Gan, David Wipf, Zheng Zhang, Christos Faloutsos, Weinan Zhang, Muhan Zhang, Zhenkun Cai, Jiahang Li, Zunyao Mao, Yakun Song, Jianheng Tang, Yanlin Zhang, Guang Yang, Chuan Lei, Xiao Qin, Ning Li, Han Zhang, Yanbo Wang, Zizhao Zhang
摘要
Given a relational database (RDB), how can we predict missing column values in some target table of interest? 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