The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. We show experimentally that a relational engine running an auto-differentiated relational algorithm can easily scale to very large datasets, and is competitive with state-of-the-art, special-purpose systems for large-scale distributed machine learning.

翻译：关系数据模型旨在促进大规模数据管理与分析。我们探讨如何对以关系方式表达的计算进行微分的问题。实验结果表明，运行自动微分关系算法的关系引擎能够轻松扩展至超大规模数据集，并在大规模分布式机器学习领域与当前最先进的专用系统具有竞争力。