摘要： 为实时显示三维数据体的海量数据，提出一种改进的海量数据快速显示算法。利用CURE聚类算法对数据进行整理，通过Hilbert R-tree对数据建立索引，根据可视化区域预测模型预测下一时刻的可视区域，以实现大量数据的快速可视化。实验结果表明，与基于视点运动的快速显示算法和基于可见性判断的可视化算法相比，该算法在不降低渲染质量的前提下，渲染速度分别提高18.27%和67.06%，预测区域错误率分别降低9.73%和22.37%，能够快速加载数据并且准确绘制大量三维数据体。

关键词: 海量数据, 希尔伯特R树, 预测模型, 聚类算法, 预加载算法

Abstract: In order to display three-dimensional massive data in real time,this paper proposes an improved rapid display algorithm for massive data.The CURE clustering algorithm is used to sort the data,and the data is indexed by Hilbert R-tree.The visual area prediction model predicts the next time visible area to realize rapid visualization of large amounts of data.Experimental results show that compared with the Visualization algorithm based on Motion of Viewpoint (VMV) and the Visualization algorithm based on Testing of Visibility (VTV),the rendering speed is 18.27% higher than the VMV algorithm without reducing the rendering quality.Compared with the VTV algorithm,the increase is 67.06%,the prediction area error rate is reduced by 9.73% compared with the VMV algorithm,and the VTV algorithm is reduced by 22.37%,which can quickly load data and accurately draw three-dimensional large data volume.

Key words: massive data, Hilbert R-tree, forecasting model, clustering algorithm, load in advance algorithm

中图分类号: 

• TP391