Deepdb: Learn from data, not from queries!
The typical approach for learned DBMS components is to capture the behavior by running a
representative set of queries and use the observations to train a machine learning model …
representative set of queries and use the observations to train a machine learning model …
Deep unsupervised cardinality estimation
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 …
capture the rich multivariate distributions of relational tables, we propose the use of a new …
Flaml: A fast and lightweight automl library
We study the problem of using low computational cost to automate the choices of learners
and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of …
and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of …
Database meets artificial intelligence: A survey
Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …
make database more intelligent (AI4DB). For example, traditional empirical database …
AI meets database: AI4DB and DB4AI
Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …
make database more intelligent (AI4DB). For example, traditional empirical database …
Are we ready for learned cardinality estimation?
Cardinality estimation is a fundamental but long unresolved problem in query optimization.
Recently, multiple papers from different research groups consistently report that learned …
Recently, multiple papers from different research groups consistently report that learned …
Deep learning models for selectivity estimation of multi-attribute queries
Selectivity estimation-the problem of estimating the result size of queries-is a fundamental
problem in databases. Accurate estimation of query selectivity involving multiple correlated …
problem in databases. Accurate estimation of query selectivity involving multiple correlated …
Learned cardinality estimation: A design space exploration and a comparative evaluation
Cardinality estimation is core to the query optimizers of DBMSs. Non-learned methods,
especially based on histograms and samplings, have been widely used in commercial and …
especially based on histograms and samplings, have been widely used in commercial and …
A survey on advancing the dbms query optimizer: Cardinality estimation, cost model, and plan enumeration
Query optimizer is at the heart of the database systems. Cost-based optimizer studied in this
paper is adopted in almost all current database systems. A cost-based optimizer introduces …
paper is adopted in almost all current database systems. A cost-based optimizer introduces …
Applications and challenges for large language models: From data management perspective
Data management is indispensable for informed decision-making in the big data era. In the
meantime, Large Language Models (LLMs), equipped with billions of model parameters and …
meantime, Large Language Models (LLMs), equipped with billions of model parameters and …