Deep learning has recently become very popular on account of its incredible success in
many complex datadriven applications, including image classification and speech …

Recent research has shown that learned models can outperform state-of-the-art index
structures in size and lookup performance. While this is a very promising result, existing …

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 …

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 …

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 …

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 …

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

Database management systems (DBMSs) are notoriously difficult to deploy and administer.
Self-driving DBMSs seek to remove these impediments by managing themselves …

The traditional and well-established cost-based query optimizer approach enumerates
different execution plans for each query, assesses each plan with costs, and selects the plan …

In line with the increasing machine learning model inference accuracy, deep learning (DL)
models have been increasingly applied to structured data for a wide spectrum of real-world …