6 papers
Query Cost Model Calibration in Confidential Virtual Machines
Qihan Zhang, Mengyuan Li, Ibrahim Sabek
With the growing adoption of Confidential Computing, running databases in confidential virtual machines (CVMs) such as AMD SEV-SNP has become an attractive way to protect sensitive…
Towards a Hybrid Quantum-Classical Computing Framework for Database Optimization Problems in Real Time Setup
Hanwen Liu, Ibrahim Sabek
Quantum computing has shown promise for solving complex optimization problems in databases, such as join ordering and index selection. Prior work often submits formulated problems…
Is Quantum Computing Ready for Real-Time Database Optimization?
Hanwen Liu, Ibrahim Sabek
Database systems encompass several performance-critical optimization tasks, such as join ordering and index tuning. As data volumes grow and workloads become more complex, these pr…
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
Hanwen Liu, Qihan Zhang, Ryan Marcus +1
Query optimization is a crucial problem in database systems that has been studied for decades. Learned query optimizers (LQOs) can improve performance over time by incorporating fe…
LIMAO: A Framework for Lifelong Modular Learned Query Optimization
Qihan Zhang, Shaolin Xie, Ibrahim Sabek
Query optimizers are crucial for the performance of database systems. Recently, many learned query optimizers (LQOs) have demonstrated significant performance improvements over tra…
Spatial Regionalization: A Hybrid Quantum Computing Approach
Yunhan Chang, Amr Magdy, Federico M. Spedalieri +1
Quantum computing has shown significant potential to address complex optimization problems; however, its application remains confined to specific problems at limited scales. Spatia…