用贝叶斯优化自动生成难测数据库查询，省去人工设计。
Adversarial Query Synthesis via Bayesian Optimization
- 通过贝叶斯优化搜索复杂查询，替代人工构造。
- 生成查询的优化空间比现有基准翻倍以上。
- 适合数据库系统评测与机器学习集成研究者。
基准工作负载对数据库管理研究至关重要，尤其在越来越多机器学习组件被集成到数据库系统的情况下。本文提出一种贝叶斯优化技术，可自动搜索具有挑战性的基准查询，显著减少通常所需的手动工作量。初步实验表明，该方法生成的查询优化头程超过现有基准的两倍以上。
原文摘要 · Abstract (English)
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.
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