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 …

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 …

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

The primordial function of a database system is to efficiently compute correct answers to
user queries. Therefore, robust query processing (RQP), where strong numerical guarantees …

Cardinality estimation is a fundamental and challenging problem in query optimization.
Recently, a number of learned models have been proposed for this task. Often, these …

Cardinality estimation of an approximate substring query is an important problem in
database systems. Traditional approaches build a summary from the text data and estimate …

Groupjoins, the combined execution of a join and a subsequent group by, are common in
analytical queries, and occur in about 1/8 of the queries in TPC-H and TPC-DS. While they …

Background. For some years now, Machine Learning (ML) has been applied to the
cardinality estimation problem [8, 12, 32, 33]. In general, ML means arbitrary function …

This paper aims to initiate a discussion around benchmarking data management systems
with machine-learned components. Traditional benchmarks such as TPC or YCSB are …

Groupjoins combine execution of a join and a subsequent group-by. They are common in
analytical queries and occur in about of the queries in TPC-H and TPC-DS. While they were …