CLC number:
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-12-10
Cited: 0
Clicked: 4019
Citations: Bibtex RefMan EndNote GB/T7714
Wei Chen, Tianye Zhang, Haiyang Zhu, Xumeng Wang, Yunhai Wang. Perspectives on cross-domain visual analysis of cyber-physical-social big data[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(12): 1559-1564.
@article{title="Perspectives on cross-domain visual analysis of cyber-physical-social big data",
author="Wei Chen, Tianye Zhang, Haiyang Zhu, Xumeng Wang, Yunhai Wang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="12",
pages="1559-1564",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100553"
}
%0 Journal Article
%T Perspectives on cross-domain visual analysis of cyber-physical-social big data
%A Wei Chen
%A Tianye Zhang
%A Haiyang Zhu
%A Xumeng Wang
%A Yunhai Wang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 12
%P 1559-1564
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100553
TY - JOUR
T1 - Perspectives on cross-domain visual analysis of cyber-physical-social big data
A1 - Wei Chen
A1 - Tianye Zhang
A1 - Haiyang Zhu
A1 - Xumeng Wang
A1 - Yunhai Wang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 12
SP - 1559
EP - 1564
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100553
Abstract: 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.
[1]Aledhari M, Razzak R, Parizi RM, et al., 2020. Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access, 8:140699-140725.
[2]Cao MQ, Liang J, Li MZ, et al., 2020. TDIVis: visual analysis of tourism destination images. Front Inform Technol Electron Eng, 21(4):536-557.
[3]Chegin M, Bernard J, Cui J, et al., 2020. Interactive visual labelling versus active learning: an experimental comparison. Front Inform Technol Electron Eng, 21(4):524-535.
[4]Deng DZ, Wu J, Wang JC, et al., 2021. EventAnchor: reducing human interactions in event annotation of racket sports videos. Proc CHI Conf on Human Factors in Computing Systems, Article 73.
[5]Giovannangeli L, Bourqui R, Giot R, et al., 2020. Toward automatic comparison of visualization techniques: application to graph visualization. Vis Inform, 4(2):86-98.
[6]He WB, Wang JP, Guo HQ, et al., 2020. CECAV-DNN: collective ensemble comparison and visualization using deep neural networks. Vis Inform, 4(2):109-121.
[7]Liu DY, Weng D, Li YH, et al., 2017. SmartAdP: visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans Vis Comput Graph, 23(1):1-10.
[8]Ma KL, Shen HW, 2020. Foreword to the Special Issue on PacificVis 2020 Workshop on Visualization Meets AI. Vis Inform, 4(2):71.
[9]Ma RX, Mei HH, Guan HH, et al., 2021. LADV: deep learning assisted authoring of dashboard visualizations from images and sketches. IEEE Trans Vis Comput Graph, 27(9):3717-3732.
[10]Manyika J, Chui M, Brown B, et al., 2011. Big Data: the Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.
[11]Meng LH, Wei YT, Pan RS, et al., 2021. VADAF: visualization for abnormal client detection and analysis in federated learning. ACM Trans Interact Intell Syst, 11(3-4):26.
[12]Munzner T, 2014. Visualization Analysis and Design. CRC Press, New York, USA.
[13]Pan JC, Han DM, Guo FZ, et al., 2020. RCAnalyzer: visual analytics of rare categories in dynamic networks. Front Inform Technol Electron Eng, 21(4):491-506.
[14]Schirner G, Erdogmus D, Chowdhury K, et al., 2013. The future of human-in-the-loop cyber-physical systems. Computer, 46(1):36-45.
[15]Tang T, Rubab S, Lai JW, et al., 2019. iStoryline: effective convergence to hand-drawn storylines. IEEE Trans Vis Comput Graph, 25(1):769-778.
[16]Tang T, Li RZ, Wu XK, et al., 2021. PlotThread: creating expressive storyline visualizations using reinforcement learning. IEEE Trans Vis Comput Graph, 27(2):294-303.
[17]Umbleja K, Ichino M, Yaguchi H, 2020. Improving symbolic data visualization for pattern recognition and knowledge discovery. Vis Inform, 4(1):23-31.
[18]Wang FY, 2010. The emergence of intelligent enterprises: from CPS to CPSS. IEEE Intell Syst, 25(4):85-88.
[19]Wang XM, Chou JK, Chen W, et al., 2018. A utility-aware visual approach for anonymizing multi-attribute tabular data. IEEE Trans Vis Comput Graph, 24(1):351-360.
[20]Wang XM, Chen W, Chou JK, et al., 2019. GraphProtector: a visual interface for employing and assessing multiple privacy preserving graph algorithms. IEEE Trans Vis Comput Graph, 25(1):193-203.
[21]Wang XM, Chen W, Xia JZ, et al., 2020a. ConceptExplorer: visual analysis of concept drifts in multi-source time-series data. IEEE Conf on Visual Analytics Science and Technology, p.1-11.
[22]Wang XM, Bryan CJ, Li YR, et al., 2020b. Umbra: a visual analysis approach for defense construction against inference attacks on sensitive information. IEEE Trans Vis Comput Graph, early access.
[23]Weng D, Zhu HM, Bao J, et al., 2018. Homefinder revisited: finding ideal homes with reachability-centric multi-criteria decision making. Proc CHI Conf on Human Factors in Computing Systems, Article 247.
[24]Xu L, 2020. Learning deep IA bidirectional intelligence. Front Inform Technol Electron Eng, 21(4):558-562.
[25]Zhang TY, Feng HZ, Chen W, et al., 2021. ChartNavigator: an interactive pattern identification and annotation framework for charts. IEEE Trans Knowl Data Eng, early access.
[26]Zhou ZH, 2016. Machine Learning. Tsinghua University Press, Beijing, China (in Chinese).
[27]Zhu SJ, Sun GD, Jiang Q, et al., 2020. A survey on automatic infographics and visualization recommendations. Vis Inform, 4(3):24-40.
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