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Cardinality estimation in dbms: A comprehensive benchmark evaluation
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 …
plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced …
Robust query driven cardinality estimation under changing workloads
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 …
lightweight, having low storage requirements, fast inference and training, and are easily …
Lero: A learning-to-rank query optimizer
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 optimizers in DBMS. While exhibiting superiority in some benchmarks, their …
FactorJoin: a new cardinality estimation framework for join queries
Cardinality estimation is one of the most fundamental and challenging problems in query
optimization. Neither classical nor learning-based methods yield satisfactory performance …
optimization. Neither classical nor learning-based methods yield satisfactory performance …
Leon: A new framework for ml-aided query optimization
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 …
With the prosperity of machine learning (ML), some recent works have shown the …
Fauce: fast and accurate deep ensembles with uncertainty for cardinality estimation
Cardinality estimation is a fundamental and critical problem in databases. Recently, many
estimators based on deep learning have been proposed to solve this problem and they have …
estimators based on deep learning have been proposed to solve this problem and they have …
Spatial Query Optimization With Learning
Query optimization is a key component in database management systems (DBMS) and
distributed data processing platforms. Recent research in the database community …
distributed data processing platforms. Recent research in the database community …
Pilotscope: Steering databases with machine learning drivers
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 …
Deploying machine learning (ML) and AI4DB algorithms into actual databases is the gold …
Stage: Query execution time prediction in amazon redshift
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 …
DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate …
Bayescard: Revitilizing bayesian frameworks for cardinality estimation
Cardinality estimation (CardEst) is an essential component in query optimizers and a
fundamental problem in DBMS. A desired CardEst method should attain good algorithm …
fundamental problem in DBMS. A desired CardEst method should attain good algorithm …