3 citations · 7 across the 8 of their papers we have counts for
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cs.DB2024★ 1 cited
The Unreasonable Effectiveness of LLMs for Query Optimization
Peter Akioyamen, Zixuan Yi, Ryan Marcus
Recent work in database query optimization has used complex machine learning strategies, such as customized reinforcement learning schemes. Surprisingly, we show that LLM embedding…
stat.ML2024★ 1 cited
A Practical Theory of Generalization in Selectivity Learning
Peizhi Wu, Haoshu Xu, Ryan Marcus +1
Query-driven machine learning models have emerged as a promising estimation technique for query selectivities. Yet, surprisingly little is known about the efficacy of these techniq…
cs.DB2024
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…