一种基于深度强化学习的数据库查询优化方法
By using a deep reinforcement learning-based approach, the database query optimization problem is transformed into a reinforcement learning task. By leveraging deep neural networks and reinforcement learning algorithms to autonomously select the optimal query execution plan, the performance degradation problem of traditional methods under complex queries and large-scale data is solved, achieving efficient query optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING INST OF TECH
- Filing Date
- 2023-10-16
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional database query optimization methods struggle to find the optimal execution plan for complex queries and large-scale data, leading to decreased query performance. Existing technologies cannot effectively handle complex combinatorial optimization problems.
This paper adopts a deep reinforcement learning-based approach to transform the query optimization problem into a reinforcement learning task. It learns query optimization strategies through deep neural networks, optimizes network parameters by combining reinforcement learning algorithms, and autonomously selects the optimal query execution plan.
It improves the performance and efficiency of database queries, achieves adaptive and intelligent query optimization, adapts to different query scenarios and data scales, handles complex query optimization problems, and finds better execution plans.
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