A recent line of works apply machine learning techniques to assist or rebuild cost-based
query optimizers in DBMS. While exhibiting superiority in some benchmarks, their …

Query optimization has long been a fundamental yet challenging topic in the database field.
With the prosperity of machine learning (ML), some recent works have shown the …

Query optimization based on deep reinforcement learning (DRL) has become a hot research
topic recently. Despite the achieved promising progress, DRL optimizers still face great …

The traditional and well-established cost-based query optimizer approach enumerates
different execution plans for each query, assesses each plan with costs, and selects the plan …

Most existing parametric query optimization (PQO) techniques rely on traditional query
optimizer cost models, which are often inaccurate and result in suboptimal query …

Learned databases, or AI4DB techniques, have rapidly developed in the last decade.
Deploying machine learning (ML) and AI4DB algorithms into actual databases is the gold …

Query performance (eg, execution time) prediction is a critical component of modern
DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate …

Compiler optimization plays an increasingly important role to boost the performance of
machine learning models for data processing and management. With increasingly complex …

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a
transformative leap in data systems. The imminent fusion of AI and DB (AI× DB) promises a …

Some recent works have shown the advantages of reinforcement learning (RL) based
learned query optimizers. These works often use the cost (ie, the estimation of cost model) or …