In this paper, we present our vision of so called zero-shot learning for databases which is a new learning approach for database components. Zero-shot learning for databases is inspired by recent advances in transfer learning of models such as GPT-3 and can support a new database out-of-the box without the need to train a new model. As a first concrete contribution in this paper, we show the feasibility of zero-shot learning for the task of physical cost estimation and present very promising initial results. Moreover, as a second contribution we discuss the core challenges related to zero-shot learning for databases and present a roadmap to extend zero-shot learning towards many other tasks beyond cost estimation or even beyond classical database systems and workloads.

中文翻译：

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一种将它们全部统治的模型：面向数据库的零攻击学习

在本文中，我们提出了对数据库零所谓学习的愿景，这是一种针对数据库组件的新学习方法。数据库零补习学习的灵感来自于模型（例如GPT-3）的转移学习的最新进展，并且可以立即支持新的数据库，而无需训练新的模型。作为本文的第一个具体贡献，我们展示了零散学习在实物成本估算任务中的可行性，并提出了非常有希望的初步结果。此外，作为第二个贡献，我们讨论了与数据库零​​击学习相关的核心挑战，并提出了将零击学习扩展到除成本估算甚至传统数据库系统和工作负载之外的许多其他任务的路线图。