Benchmark workloads are extremely important to the database management research community, especially as more machine learning components are integrated into database systems. Here, we propose a Bayesian optimization technique to automatically search for difficult benchmark queries, significantly reducing the amount of manual effort usually required. In preliminary experiments, we show that our approach can generate queries with more than double the optimization headroom compared to existing benchmarks.

翻译：基准测试工作负载对数据库管理研究界极为重要，随着更多机器学习组件被集成到数据库系统中尤为如此。本文提出一种贝叶斯优化技术，能够自动搜索具有挑战性的基准测试查询，显著减少通常所需的人工工作量。在初步实验中，我们证明该方法生成的查询相较于现有基准测试，其优化空间可提升两倍以上。