382 citations · 850 across the 10 of their papers we have counts for
10 papers
Multi-agent Databases via Independent Learning
Chi Zhang, Olga Papaemmanouil, Josiah P. Hanna +1
Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, phys…
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning
Chi Zhang, Ryan Marcus, Anat Kleiman +1
In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance. We introd…
Neo: A Learned Query Optimizer
Ryan Marcus, Parimarjan Negi, Hongzi Mao +5
Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex component…
Plan-Structured Deep Neural Network Models for Query Performance Prediction
Ryan Marcus, Olga Papaemmanouil
Query performance prediction, the task of predicting the latency of a query, is one of the most challenging problem in database management systems. Existing approaches rely on feat…
Flexible Operator Embeddings via Deep Learning
Ryan Marcus, Olga Papaemmanouil
Integrating machine learning into the internals of database management systems requires significant feature engineering, a human effort-intensive process to determine the best way…
Towards a Hands-Free Query Optimizer through Deep Learning
Ryan Marcus, Olga Papaemmanouil
Query optimization remains one of the most important and well-studied problems in database systems. However, traditional query optimizers are complex heuristically-driven systems,…
Deep Reinforcement Learning for Join Order Enumeration
Ryan Marcus, Olga Papaemmanouil
Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join enumeration algorithms that do not receive any fee…
OptMark: A Toolkit for Benchmarking Query Optimizers
Zhan Li, Olga Papaemmanouil, Mitch Cherniack
Query optimizers have long been considered as among the most complex components of a database engine, while the assessment of an optimizer's quality remains a challenging task. Ind…
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases
Ryan Marcus, Olga Papaemmanouil
Workload management for cloud databases must deal with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance…
AIDE: An Automated Sample-based Approach for Interactive Data Exploration
Kyriaki Dimitriadou, Olga Papaemmanouil, Yanlei Diao
In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an a…