Transition Systems from Causal Reversible Bundle Event Structures
本文研究了具有因果可逆性的捆绑事件结构，通过开发到两种转换系统语义的映射来证明这些语义之间的同构性。
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Training-Free Query Optimization via LLM-Based Plan Similarity
Traditional query optimizers rely on error-prone cost models, while learned optimizers require costly offline training and lack deployment flexibility. Method: We propose a training-free, LLM-embedding-driven execution plan optimization framework. It leverages pre-trained large language models to map SQL queries and their execution plans into a unified embedding space; retrieves semantically similar historical queries via k-nearest-neighbor search; recommends efficient hint sets through majority voting; and ensures robustness via lightweight consistency checking and full-plan-space fallback. Contribution/Results: This is the first work to employ LLM-derived execution plan embeddings for training-free query optimization. It enables end-to-end automatic hint recommendation and achieves deep integration with OpenGauss. Evaluated on the JOB-CEB benchmark, our approach reduces average query latency by 21%, significantly enhancing both practicality and deployment efficiency.
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Transition Systems from Causal Reversible Bundle Event Structures
本文研究了具有因果可逆性的捆绑事件结构，通过开发到两种转换系统语义的映射来证明这些语义之间的同构性。
0 citationsRead paper
Training-Free Query Optimization via LLM-Based Plan Similarity
Traditional query optimizers rely on error-prone cost models, while learned optimizers require costly offline training and lack deployment flexibility. Method: We propose a training-free, LLM-embedding-driven execution plan optimization framework. It leverages pre-trained large language models to map SQL queries and their execution plans into a unified embedding space; retrieves semantically similar historical queries via k-nearest-neighbor search; recommends efficient hint sets through majority voting; and ensures robustness via lightweight consistency checking and full-plan-space fallback. Contribution/Results: This is the first work to employ LLM-derived execution plan embeddings for training-free query optimization. It enables end-to-end automatic hint recommendation and achieves deep integration with OpenGauss. Evaluated on the JOB-CEB benchmark, our approach reduces average query latency by 21%, significantly enhancing both practicality and deployment efficiency.
0 citationsRead paper