Perspectives on cross-domain visual analysis of cyber-physical-social big data

被引:5
作者
Chen, Wei [1 ]
Zhang, Tianye [1 ]
Zhu, Haiyang [1 ]
Wang, Xumeng [1 ]
Wang, Yunhai [2 ]
机构
[1] Zhejiang Univ, State Key Lab CAD & CC, Hangzhou 310058, Peoples R China
[2] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China
基金
中国国家自然科学基金;
关键词
VISUALIZATION;
D O I
10.1631/FITEE.2100553
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The domain of cyber-physical-social (CPS) big data is generally defined as the set consisting of all the elements in its defined domain, including domains of data, objects, tasks, application scenarios, and subjects. Visual analytics is an emerging human-in-the-loop big data analytics paradigm that can exploit human perception to enhance human cognitive efficiency. In this paper, we explore the perspectives on cross-domain visual analysis of CPS big data. We also highlight new challenges brought by the cross-domain nature of CPS big data-data, subject, and task domains-and propose a novel visual analytics model and a suite of approaches to address these challenges.
引用
收藏
页码:1559 / 1564
页数:6
相关论文
共 27 条
[1]  
Aledhari M, 2020, IEEE ACCESS, V8, P140699, DOI [10.1109/ACCESS.2020.3013541, 10.1109/access.2020.3013541]
[2]   TDIVis: visual analysis of tourism destination images [J].
Cao, Meng-qi ;
Liang, Jing ;
Li, Ming-zhao ;
Zhou, Zheng-hao ;
Zhu, Min .
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 2020, 21 (04) :536-557
[3]   Interactive visual labelling versus active learning: an experimental comparison [J].
Chegini, Mohammad ;
Bernard, Jurgen ;
Cui, Jian ;
Chegini, Fatemeh ;
Sourin, Alexei ;
Andrews, Keith ;
Schreck, Tobias .
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 2020, 21 (04) :524-535
[4]  
Deng D., 2021, P 2021 CHI C HUM FAC, P1, DOI DOI 10.1145/3411764.34454312
[5]   Toward automatic comparison of visualization techniques: Application to graph visualization [J].
Giovannangeli, L. ;
Bourqui, R. ;
Giot, R. ;
Auber, D. .
VISUAL INFORMATICS, 2020, 4 (02) :86-98
[6]   CECAV-DNN: Collective Ensemble Comparison and Visualization using Deep Neural Networks [J].
He, Wenbin ;
Wang, Junpeng ;
Guo, Hanqi ;
Shen, Han-Wei ;
Peterka, Tom .
VISUAL INFORMATICS, 2020, 4 (02) :109-121
[7]   SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations [J].
Liu, Dongyu ;
Weng, Di ;
Li, Yuhong ;
Bao, Jie ;
Zheng, Yu ;
Qu, Huamin ;
Wu, Yingcai .
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2017, 23 (01) :1-10
[8]   Foreword to the Special Issue on PacificVis 2020 Workshop on Visualization Meets AI [J].
Ma, Kwan-Liu ;
Shen, Han-Wei .
VISUAL INFORMATICS, 2020, 4 (02) :71-71
[9]   LADV: Deep Learning Assisted Authoring of Dashboard Visualizations From Images and Sketches [J].
Ma, Ruixian ;
Mei, Honghui ;
Guan, Huihua ;
Huang, Wei ;
Zhang, Fan ;
Xin, Chengye ;
Dai, Wenzhuo ;
Wen, Xiao ;
Chen, Wei .
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2021, 27 (09) :3717-3732
[10]  
Manyika J., 2011, big data: the next frontier for innovation, competition, and pro-ductivity