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Training-Free Query Optimization via LLM-Based Plan Similarity
Nikita Vasilenko, Alexander Demin, Vladimir Boorlakov
Large language model (LLM) embeddings offer a promising new avenue for database query optimization. In this paper, we explore how pre-trained execution plan embeddings can guide SQ…
Learning-Augmented Online Caching: New Upper Bounds
Daniel Skachkov, Denis Ponomaryov, Yuri Dorn +1
We address the problem of learning-augmented online caching in the scenario when each request is accompanied by a prediction of the next occurrence of the requested page. We improv…
Adaptive Cost Model for Query Optimization
Nikita Vasilenko, Alexander Demin, Denis Ponomaryov
The principal component of conventional database query optimizers is a cost model that is used to estimate expected performance of query plans. The accuracy of the cost model has d…
EEvA: Fast Expert-Based Algorithms for Buffer Page Replacement
Alexander Demin, Yuriy Dorn, Aleksandr Katrutsa +4
Optimal page replacement is an important problem in efficient buffer management. The range of replacement strategies known in the literature varies from simple but efficient FIFO-b…