Database meets deep learning: Challenges and opportunities
Deep learning has recently become very popular on account of its incredible success in
many complex datadriven applications, including image classification and speech …
many complex datadriven applications, including image classification and speech …
Bao: Making learned query optimization practical
Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …
practical gains due to substantive training overhead, inability to adapt to changes, and poor …
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 …
NeuroCard: one cardinality estimator for all tables
Query optimizers rely on accurate cardinality estimates to produce good execution plans.
Despite decades of research, existing cardinality estimators are inaccurate for complex …
Despite decades of research, existing cardinality estimators are inaccurate for complex …
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 …
RadixSpline: a single-pass learned index
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 …
structures in size and lookup performance. While this is a very promising result, existing …
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 …
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 …
Updatable learned index with precise positions
Index plays an essential role in modern database engines to accelerate the query
processing. The new paradigm of" learned index" has significantly changed the way of …
processing. The new paradigm of" learned index" has significantly changed the way of …