382 citations · 658 across the 6 of their papers we have counts for
6 papers
Stage: Query Execution Time Prediction in Amazon Redshift
Ziniu Wu, Ryan Marcus, Zhengchun Liu +7
Query performance (e.g., execution time) prediction is a critical component of modern DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate execution…
FactorJoin: A New Cardinality Estimation Framework for Join Queries
Ziniu Wu, Parimarjan Negi, Mohammad Alizadeh +2
Cardinality estimation is one of the most fundamental and challenging problems in query optimization. Neither classical nor learning-based methods yield satisfactory performance wh…
Flow-Loss: Learning Cardinality Estimates That Matter
Parimarjan Negi, Ryan Marcus, Andreas Kipf +4
Previous approaches to learned cardinality estimation have focused on improving average estimation error, but not all estimates matter equally. Since learned models inevitably make…
Bao: Learning to Steer Query Optimizers
Ryan Marcus, Parimarjan Negi, Hongzi Mao +3
Query optimization remains one of the most challenging problems in data management systems. Recent efforts to apply machine learning techniques to query optimization challenges hav…
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…
High Throughput Cryptocurrency Routing in Payment Channel Networks
Vibhaalakshmi Sivaraman, Shaileshh Bojja Venkatakrishnan, Kathy Ruan +5
Despite growing adoption of cryptocurrencies, making fast payments at scale remains a challenge. Payment channel networks (PCNs) such as the Lightning Network have emerged as a via…