首次理论分析学习型数据库操作在分布漂移下的表现
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability
- 提出分布可学习性框架，分析模型在数据分布变化时的表现
- 给出学习模型性能的理论边界，证明其在特定条件下优于传统方法
- 为未来学习型数据库操作研究提供理论基础，适合数据库与机器学习交叉研究者
利用机器学习执行数据库操作（如索引、基数估计、排序）已被证明能显著提升性能。然而，当数据集变化导致数据分布漂移时，学习模型的性能也会下降，甚至可能劣于非学习方法。这与缺乏对学习方法的理论理解共同限制了其实际应用，因为部署后无法保证性能。本文首次对动态数据集中上述操作的学习模型性能进行理论表征。结果揭示了学习模型的新理论特性，并给出了其性能的理论界，说明了学习模型在何种情况下能优于传统方法。分析构建了分布可学习性框架及新颖的理论工具，为未来学习型数据库操作的研究奠定基础。
原文摘要 · Abstract (English)
Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits. However, when datasets change and data distribution shifts, empirical results also show performance degradation for learned models, possibly to worse than non-learned alternatives. This, together with a lack of theoretical understanding of learned methods undermines their practical applicability, since there are no guarantees on how well the models will perform after deployment. In this paper, we present the first known theoretical characterization of the performance of learned models in dynamic datasets, for the aforementioned operations. Our results show novel theoretical characteristics achievable by learned models and provide bounds on the performance of the models that characterize their advantages over non-learned methods, showing why and when learned models can outperform the alternatives. Our analysis develops the distribution learnability framework and novel theoretical tools which build the foundation for the analysis of learned database operations in the future.
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