Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

作     者：Yang Yang Kecheng Lu Yu Wu Yunhai Wang Yi Cao Yang Yang;Kecheng Lu;Yu Wu;Yunhai Wang;Yi Cao

作者机构：Institute of Applied Physics and Computational MathematicsBeijing 100094China School of Computer Science and TechnologyShandong UniversityQingdao 266237China 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2023年第9卷第3期

页      面：513-529页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the Chinese Postdoctoral Science Foundation(2021M700016) 

主　　题：correlation-awareness large-scale data multi-block methods probabilistic data summarization 

摘      要：In this paper,we propose a correlationaware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific *** core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information,spatial location,and correlation distribution using Bayes’*** effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them,thus significantly reducing the computational ***,this enables reconstruction of the original data more accurately than existing *** demonstrate the effectiveness of our technique using six datasets,with the largest having one billion grid *** experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.