<ul><li>Researchers have introduced LimeQO, a framework for offline query optimization, aiming to reduce the resource usage in learned query optimizers.</li><li>LimeQO leverages low-rank learning to efficiently explore alternative query plans and predicts unobserved query plan latencies using purely linear methods.</li><li>The framework models the workload as a partially observed, low-rank matrix, significantly reducing computational overhead compared to neural networks.</li><li>LimeQO provides a low-overhead solution and a no-regressions guarantee without making assumptions about the underlying DBMS.</li></ul>

Low Rank Learning for Offline Query Optimization

Discover more