Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …

Cardinality estimation (CardEst) plays a significant role in generating high-quality query
plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced …

Query driven cardinality estimation models learn from a historical log of queries. They are
lightweight, having low storage requirements, fast inference and training, and are easily …

Large language models (LLMs) have shown superior performance in various areas. And
LLMs have the potential to revolutionize data management by serving as the" brain" of next …

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 …

Traditional cost-based optimizers are efficient and stable to generate optimal plans for
simple SQL queries, but they may not generate high-quality plans for complicated queries …

Learned cardinality estimation (CE) has recently gained significant attention for replacing
long-studied traditional CE with machine learning, especially for deep learning. However …

Cardinality estimation is a fundamental but long unresolved problem in query optimization.
Recently, multiple papers from different research groups consistently report that learned …

Query optimizers are a performance-critical component in every database system. Due to
their complexity, optimizers take experts months to write and years to refine. In this work, we …

Cardinality estimation is one of the most fundamental and challenging problems in query
optimization. Neither classical nor learning-based methods yield satisfactory performance …