https://dasya.itu.dk/supervisor/xiao-li/ Recent content in Xiao Li on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Mon, 24 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-xli-dataprepare4qo/ Mon, 24 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-xli-dataprepare4qo/ Traditional cost-based query optimizers are highly effective for many database workloads, especially for short and moderately complex queries. However, for queries involving many joins and large search spaces, optimization can become expensive and inaccurate. Recent learning-based query optimizers have shown promising results for complex queries, but they may introduce unnecessary overhead for simpler queries. This project aims to develop a hybrid query optimizer that combines the advantages of both approaches. The main idea is to analyze an incoming query and dynamically decide whether it should be handled by a traditional cost-based optimizer or a learning-based optimizer.