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 …

Most existing parametric query optimization (PQO) techniques rely on traditional query
optimizer cost models, which are often inaccurate and result in suboptimal query …

Query plan is widely used as input in machine learning for databases (ML4DB) research,
with query plan representation as a critical step. However, existing studies typically focus on …

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

Learned database systems address several weaknesses of traditional cost estimation
techniques in query optimization: they learn a model of a database instance, eg, as queries …

The optimization of queries speeds up query processing in databases. One of the most time-
consuming tasks in query processing is the join operation, where the order of the joins plays …

The ever-growing collections of data series create a pressing need for efficient similarity
search, which serves as the backbone for various analytics pipelines. Recent studies have …

Modern database management systems (DBMS), primarily designed as general-purpose
systems, face the challenging task of efficiently handling data from diverse sources for both …

Cardinality estimation is one of the most important problems in query optimization. Recently,
machine learning-based techniques have been proposed to effectively estimate cardinality …

Metrics for set similarity are a core aspect of several data mining tasks. To remove duplicate
results in a Web search, for example, a common approach looks at the Jaccard index …