Ryan Marcus | DeepAI

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∙ 06/11/2023

Kepler: Robust Learning for Faster Parametric Query Optimization

Most existing parametric query optimization (PQO) techniques rely on tra...

0 Lyric Doshi, et al. ∙

∙ 03/27/2023

Learned Query Superoptimization

Traditional query optimizers are designed to be fast and stateless: each...

0 Ryan Marcus, et al. ∙

∙ 11/03/2022

AdaChain: A Learned Adaptive Blockchain

This paper presents AdaChain, a learning-based blockchain framework that...

0 Chenyuan Wu, et al. ∙

∙ 05/11/2022

LSI: A Learned Secondary Index Structure

Learned index structures have been shown to achieve favorable lookup per...

0 Andreas Kipf, et al. ∙

∙ 08/11/2021

Towards Practical Learned Indexing

Latest research proposes to replace existing index structures with learn...

0 Mihail Stoian, et al. ∙

∙ 05/18/2021

LEA: A Learned Encoding Advisor for Column Stores

Data warehouses organize data in a columnar format to enable faster scan...

0 Lujing Cen, et al. ∙

∙ 01/13/2021

Flow-Loss: Learning Cardinality Estimates That Matter

Previous approaches to learned cardinality estimation have focused on im...

0 Parimarjan Negi, et al. ∙

∙ 10/12/2020

Class-Weighted Evaluation Metrics for Imbalanced Data Classification

Class distribution skews in imbalanced datasets may lead to models with ...

23 Akhilesh Gupta, et al. ∙

∙ 07/21/2020

Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning

In this extended abstract, we propose a new technique for query scheduli...

0 Chi Zhang, et al. ∙

∙ 06/23/2020

Benchmarking Learned Indexes

Recent advancements in learned index structures propose replacing existi...

0 Ryan Marcus, et al. ∙

∙ 06/05/2020

MISIM: An End-to-End Neural Code Similarity System

Code similarity systems are integral to a range of applications from cod...

6 Fangke Ye, et al. ∙

∙ 04/30/2020

RadixSpline: A Single-Pass Learned Index

Recent research has shown that learned models can outperform state-of-th...

0 Andreas Kipf, et al. ∙

∙ 04/28/2020

Learned Garbage Collection

Several programming languages use garbage collectors (GCs) to automatica...

0 Lujing Cen, et al. ∙

∙ 04/08/2020

Bao: Learning to Steer Query Optimizers

Query optimization remains one of the most challenging problems in data ...

0 Ryan Marcus, et al. ∙

∙ 03/24/2020

Context-Aware Parse Trees

The simplified parse tree (SPT) presented in Aroma, a state-of-the-art c...

7 Fangke Ye, et al. ∙

∙ 03/21/2020

ARDA: Automatic Relational Data Augmentation for Machine Learning

Automatic machine learning () is a family of techniques to automate the ...

17 Nadiia Chepurko, et al. ∙

∙ 11/29/2019

SOSD: A Benchmark for Learned Indexes

A groundswell of recent work has focused on improving data management sy...

0 Andreas Kipf, et al. ∙

∙ 04/07/2019

Neo: A Learned Query Optimizer

Query optimization is one of the most challenging problems in database s...

0 Ryan Marcus, et al. ∙

∙ 01/31/2019

Plan-Structured Deep Neural Network Models for Query Performance Prediction

Query performance prediction, the task of predicting the latency of a qu...

0 Ryan Marcus, et al. ∙

∙ 01/25/2019

Flexible Operator Embeddings via Deep Learning

Integrating machine learning into the internals of database management s...

0 Ryan Marcus, et al. ∙

∙ 09/26/2018

Towards a Hands-Free Query Optimizer through Deep Learning

Query optimization remains one of the most important and well-studied pr...

0 Ryan Marcus, et al. ∙

∙ 02/28/2018

Deep Reinforcement Learning for Join Order Enumeration

Join order selection plays a significant role in query performance. Many...

0 Ryan Marcus, et al. ∙

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