R-tree的查询代价模型分析及算法改进

Cost Model of R-tree and Algorithmic Optimization

摘要: 提出一个R-tree的查询代价模型(Cost Model),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Cost Model和CR^*-tree做了实验,结果显示该Cost Model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4.25%.

Abstract: 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.