Delaunay Triangulation in the Big Data Landscape: A Parallel Optimization Approach

被引：0

作者：

Zhou, Shuqiang ^{[1
]}

Wang, Yankun ^{[2
]}

机构：

[1] Hebi Institute of Engineering and Technology, Henan Polytechnic University, Hebi

[2] Internet of Things Research Institute, Shenzhen Polytechnic University, Shenzhen

来源：

Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Big Data Analysis; Delaunay triangulation; Insertion point method; Parallel algorithm;

D O I：

10.2478/amns-2024-2635

中图分类号：

学科分类号：

摘要：

In the era of big data, from digital cities to digital earth, the exponential growth of spatial information due to the development of diverse data collection technologies has been a significant concern.Delaunay triangulation has garnered widespread attention and application in geomorphological analysis, topographic simulation, and cartographic synthesis due to its minimal data redundancy and excellent stability.However, as the application fields of Delaunay triangular mesh models continue to expand and application requirements deepen, especially with the urgent need to address real-time large-scale scene rendering and terrain visualization, the efficiency, accuracy, and stability of Delaunay triangulation meshes are increasingly demanded.This paper proposes a parallel optimization algorithm based on the insertion point method, following an analysis of the traditional insertion point method, and demonstrates its effectiveness through a series of experiments. © 2024 Shuqiang Zhou and Yankun Wang,

引用

共 24 条

[1]

Wang Q., Kim M., Shi Y., Et al., Predict brain MR image registration via sparse learning of appearance and transformation, Medical Image Analysis, 20, 1, pp. 61-75, (2015)

[2]

Qin K., Wu T., Exploration of intelligent spatial information processing and spatial-temporal big data analysis, Geospatial Information, 20, 12, pp. 1-11, (2022)

[3]

Wang Y., Zeng H., Xu L., Et al., A review of JVM optimization techniques for big data processing frameworks, Software Journal, 34, 1, pp. 463-488, (2023)

[4]

Wang B., Wu H., Liu M., Resource allocation applied to flexible printed circuit routing based on constrained Delaunay triangulation, Integration, 87, pp. 16-23, (2022)

[5]

Song T., Wang J., Xu D., Wei W., Han R., Meng F., Li Y., Xie P., Unsupervised machine learning for improved Delaunay triangulation, Journal of Marine Science and Engineering, 9, 12, (2021)

[6]

Li G., Li Z., Noisy point cloud surface reconstruction using Delaunay subdivision, Journal of Wuhan University (Information Science Edition), 42, 1, pp. 123-129, (2017)

[7]

Xiao B., Wang L., Study on the integration of land and water topographic data of Poyang Lake by Delaunay triangular network, Science of Surveying and Mapping, 44, 1, pp. 48-52, (2019)

[8]

Zhang X., Lu Z., 3D parallel constrained Delaunay mesh generation algorithm and implementation, Journal of Computer-Aided Design & Computer Graphics, 28, 6, pp. 932-942, (2016)

[9]

Cui C., Chen X., Global station selection algorithm for spherical Delaunay triangular networks and its application, Journal of Geomatics Science and Technology, 38, 5, pp. 466-469, (2021)

[10]

You L., Yan C., Parallel Delaunay triangular mesh growth algorithm with adaptive dichotomy, Journal of Xinyang Normal University (Natural Science Edition), 35, 4, pp. 656-663, (2022)

← 1 2 3 →