论文标题
NOSQL数据库的索引选择，并具有深入的增强学习
Index Selection for NoSQL Database with Deep Reinforcement Learning
论文作者
论文摘要
我们提出了一种NOSQL数据库索引选择的新方法。对于不同的工作负载，我们选择不同的索引及其不同的参数来优化数据库性能。该方法构建了一个深厚的增强学习模型，以选择给定固定工作负载的最佳索引并适应不断变化的工作负载。实验结果表明，根据传统的单个索引结构，深度强化学习指数选择方法（DRLISA）在不同程度上提高了性能。
We propose a new approach of NoSQL database index selection. For different workloads, we select different indexes and their different parameters to optimize the database performance. The approach builds a deep reinforcement learning model to select an optimal index for a given fixed workload and adapts to a changing workload. Experimental results show that, Deep Reinforcement Learning Index Selection Approach (DRLISA) has improved performance to varying degrees according to traditional single index structures.
