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.
翻译:在本文中,我们提出了我们所谓的数据库零点学习的愿景,这是数据库组成部分的一种新的学习方法。数据库零点学习的灵感来自诸如GPT-3等模型的转让学习的最新进展,可以支持新的数据库外的新的数据库,而无需培训新的模型。作为本文件的第一个具体贡献,我们展示了实际成本估算任务零点学习的可行性,并提出了非常有希望的初步成果。此外,作为第二稿,我们讨论了与数据库零点学习有关的核心挑战,并提出了将零点学习扩大到成本估算或甚至超出传统数据库系统和工作量以外的许多其他任务的路线图。