WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos

• URL: http://arxiv.org/abs/2505.16635v2
• Date: Mon, 27 Oct 2025 12:12:05 GMT
• Title: WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos
• Authors: Zhaomin Wu, Ziyang Wang, Bingsheng He,
• Abstract summary: Collaborative learning (CL) techniques enable multiple parties to train models jointly without sharing raw data.<n>Current CL benchmarks and algorithms primarily target the learning step under assumptions of isolated, aligned, and joinable databases.<n>We build a large-scale dataset constructed from 100,000 real-world relational databases linked by 17 million weighted edges.
• Score: 48.88393315169039
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relational databases are often fragmented across organizations, creating data silos that hinder distributed data management and mining. Collaborative learning (CL) -- techniques that enable multiple parties to train models jointly without sharing raw data -- offers a principled approach to this challenge. However, existing CL frameworks (e.g., federated and split learning) remain limited in real-world deployments. Current CL benchmarks and algorithms primarily target the learning step under assumptions of isolated, aligned, and joinable databases, and they typically neglect the end-to-end data management pipeline, especially preprocessing steps such as table joins and data alignment. In contrast, our analysis of the real-world corpus WikiDBs shows that databases are interconnected, unaligned, and sometimes unjoinable, exposing a significant gap between CL algorithm design and practical deployment. To close this evaluation gap, we build WikiDBGraph, a large-scale dataset constructed from 100{,}000 real-world relational databases linked by 17 million weighted edges. Each node (database) and edge (relationship) is annotated with 13 and 12 properties, respectively, capturing a hybrid of instance- and feature-level overlap across databases. Experiments on WikiDBGraph demonstrate both the effectiveness and limitations of existing CL methods under realistic conditions, highlighting previously overlooked gaps in managing real-world data silos and pointing to concrete directions for practical deployment of collaborative learning systems.

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