4 papers
Prune, Don't Rebuild: Efficiently Tuning -Reachable Graphs for Nearest Neighbor Search
Tian Zhang, Ashwin Padaki, Jiaming Liang +2
Vector similarity search is an essential primitive in modern AI and ML applications. Most vector databases adopt graph-based approximate nearest neighbor (ANN) search algorithms, s…
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 (…
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…
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…