3 citations · 6 across the 3 of their papers we have counts for
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cs.DB2025★ 2 cited
Data-Agnostic Cardinality Learning from Imperfect Workloads
Peizhi Wu, Rong Kang, Tieying Zhang +3
Cardinality estimation (CardEst) is a critical aspect of query optimization. Traditionally, it leverages statistics built directly over the data. However, organizational policies (…
cs.DB2025★ 3 cited
Low Rank Learning for Offline Query Optimization
Zixuan Yi, Yao Tian, Zachary G. Ives +1
Recent deployments of learned query optimizers use expensive neural networks and ad-hoc search policies. To address these issues, we introduce \textsc{LimeQO}, a framework for offl…
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…