Adversarial Query Synthesis via Bayesian Optimization
This addresses the need for efficient benchmark generation in database management research, especially with increasing ML integration, though it appears incremental as it builds on existing optimization methods.
The paper tackles the problem of manually creating difficult benchmark queries for database systems by proposing a Bayesian optimization technique to automatically search for such queries, resulting in generated queries with more than double the optimization headroom compared to existing benchmarks.
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.