R-tree的查询代价模型分析及算法改进

Cost Model of R-tree and Algorithmic Optimization

提出一个R tree的查询代价模型 (CostModel) ,在对该模型分析的基础上 ,对R tree及其变种进行了改进 ,形成了CR tree 分别对CostModel和CR tree做了实验 ,结果显示该CostModel的平均误差为 12 6 %,而改进后的CR tree查询性能比R tree提高了 4 2 5 %

We present a cost model for predicting the performance of R-tree and its variants. Optimization based on the cost model can be applied to R-tree construction. We construct a new R-tree variant named CR *-tree using this cost model. Experiment results show that the relative error of the cost model is around 12.6%, and the performance for querying CR *-tree has been improved 4.25% in contrast with R *-tree's.