In this paper, we introduce zero-shot cost models which enable learned cost
estimation that generalizes to unseen databases. In contrast to
state-of-the-art workload-driven approaches which require to execute a large
set of training queries on every new database, zero-shot cost models thus allow
to instantiate a learned cost model out-of-the-box without expensive training
data collection. To enable such zero-shot cost models, we suggest a new
learning paradigm based on pre-trained cost models. As core contributions to
support the transfer of such a pre-trained cost model to unseen databases, we
introduce a new model architecture and representation technique for encoding
query workloads as input to those models. As we will show in our evaluation,
zero-shot cost estimation can provide more accurate cost estimates than
state-of-the-art models for a wide range of (real-world) databases without
requiring any query executions on unseen databases. Furthermore, we show that
zero-shot cost models can be used in a few-shot mode that further improves
their quality by retraining them just with a small number of additional
training queries on the unseen database.

本文提出了零射模型成本，利用预训练模型提供更精确的成本估计，而无需在未知数据库上执行任何查询，避免了昂贵的数据收集开销。深度学习架构及查询工作量的表示技术也被本文提出用以支持预训练模型的转移，进一步提高了模型的质量及精度。而可在少数查询的少数 - shot 应用程序中，使用预训练成本模型进行改进。