Cardinality estimation has long been grounded in statistical tools for density estimation. To
capture the rich multivariate distributions of relational tables, we propose the use of a new …

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 …

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 rely on accurate cardinality estimation (CardEst) to produce good
execution plans. The core problem of CardEst is how to model the rich joint distribution of …

Machine learning (ML) has become the de-facto approach for various scientific domains
such as computer vision and natural language processing. Despite recent breakthroughs …

Previous approaches to learned cardinality estimation have focused on improving average
estimation error, but not all estimates matter equally. Since learned models inevitably make …

Cardinality estimation (CardEst) is an essential component in query optimizers and a
fundamental problem in DBMS. A desired CardEst method should attain good algorithm …

In this paper, we propose a simple, yet fast and effective approach to determine good join
orders for arbitrary selectproject-join queries. Our scheme comprises three building …

We introduce LSQB, a new large-scale subgraph query benchmark. LSQB tests the
performance of database management systems on an important class of subgraph queries …

Recent efforts in learned cardinality estimation (CE) have substantially improved estimation
accuracy and query plans inside query optimizers. However, achieving decent efficiency …