Predicting query cost plays an important role in moving object databases. Accurate predictions help database administrators effectively schedule workloads and achieve optimal resource allocation strategies. There are some works focusing on query cost prediction, but most of them employ analytical methods to obtain an index-based cost prediction model. The accuracy can be seriously challenged as the workload of the database management system becomes more and more complex. Differing from the previous work, this paper proposes a method called CPRQ (Cost Prediction of Range Query) which is based on machine-learning techniques. The proposed method contains four learning models: the polynomial regression model, the decision tree regression model, the random forest regression model, and the KNN (k-Nearest Neighbor) regression model. Using R-squared and MSE (Mean Squared Error) as measurements, we perform an extensive experimental evaluation. The results demonstrate that CPRQ achieves high accuracy and the random forest regression model obtains the best predictive performance (R-squared is 0.9695 and MSE is 0.154).

中文翻译：

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CPRQ：移动对象数据库中范围查询的成本预测

预测查询成本在移动对象数据库中起着重要作用。准确的预测有助于数据库管理员有效地安排工作负载并实现最佳的资源分配策略。有一些工作专注于查询成本预测，但大多数都采用分析方法来获得基于索引的成本预测模型。随着数据库管理系统的工作量变得越来越复杂，准确性会受到严重挑战。与之前的工作不同，本文提出了一种基于机器学习技术的称为 CPRQ（范围查询的成本预测）的方法。所提出的方法包含四种学习模型：多项式回归模型、决策树回归模型、随机森林回归模型和KNN（k-最近邻）回归模型。使用 R 平方和 MSE（均方误差）作为测量值，我们进行了广泛的实验评估。结果表明，CPRQ 实现了高精度，随机森林回归模型获得了最好的预测性能（R-squared 为 0.9695，MSE 为 0.154）。