4 papers
URecJPQ: Memory-efficient Multimodal Recommendation Models through RecJPQ in Large-Scale Scenarios
Giuseppe Spillo, Zixuan Yi, Aleksandr Petrov +3
Training state-of-the-art recommendation models on large-scale industrial datasets can be a challenging task due to the high number of users and items which are typically represent…
Is it Bigger than a Breadbox: Efficient Cardinality Estimation for Real World Workloads
Zixuan Yi, Sami Abu-el-Haija, Yawen Wang +8
DB engines produce efficient query execution plans by relying on cost models. Practical implementations estimate cardinality of queries using heuristics, with magic numbers tuned t…
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…
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…