时序数据跨域关联可视分析

doi:10.16182/j.issn1004731x.joss.20-0659

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (1): 53-61.doi: 10.16182/j.issn1004731x.joss.20-0659

时序数据跨域关联可视分析

韩贝贝, 魏迎梅^*, 方玉杰, 万珊珊

国防科技大学系统工程学院,湖南长沙 410073

收稿日期:2020-09-03 修回日期:2020-12-15 出版日期:2022-01-18 发布日期:2022-01-14
通讯作者: 魏迎梅(1972-),女,博士,教授,研究方向为大数据可视化与可视分析。E-mail：weiyingmei@nudt.edu.cn
作者简介:韩贝贝(1989-)，女，博士生，研究方向为可视分析和复杂网络分析。E-mail：hanbei969@163.com
基金资助:
国家自然科学基金(61873274); 湖南省研究生科研创新项目(CX20200075)

Visual Analysis of Cross-domain Association of Time-series Data

Han Beibei, Wei Yingmei^*, Fang Yujie, Wan Shanshan

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Received:2020-09-03 Revised:2020-12-15 Online:2022-01-18 Published:2022-01-14

摘要/Abstract

摘要： 时序数据是数据挖掘的一类重要研究对象,当前的时序数据可视分析技术很少进行跨域关联,用户无法在统一视图中同时关注时空域的统计时序数据与认知域的文本主题数据的发展演化。抽象出用户基于跨域时序数据的可视分析过程,提出一种可视分析流程模型;以该模型为指导,设计了多视图协同的跨域关联可视分析工具。通过疫情时序数据和舆情文本数据的实例分析,证明了本方法可以较好的支撑用户同时关注不同域时序数据的发展演化,快速探索数据中隐含的特征信息和时空变化规律。

关键词: 时序数据, 文本主题数据, 时空域, 认知域, 跨域关联, 可视分析

Abstract: Time series data is the important research object of data mining. The current visual analysis technology of time series data rarely conducts the cross-domain correlation. The development and evolution of statistical time series data in the spatio-temporal domain and the text theme data in the cognitive domain cannot be simulitaneously supervised in a unified view. The user's visual analysis process based on cross-domain time series data is abstracted and a visual analysis process model is proposed. A multi-view collaborative cross-domain correlation visual analysis tool is designed on the basis of the model. The case study of epidemic time series data and public opinion text data show that the method can well support users to supervise the development and evolution of the time series data in different domain at the same time, and quickly explore the characteristic information and temporal and spatial changes of the data.

Key words: time series data, text theme data, spatio-temporal domain, cognitive domain, cross domain correlation, visual analytics

中图分类号:

TP391.9

韩贝贝, 魏迎梅, 方玉杰, 万珊珊. 时序数据跨域关联可视分析[J]. 系统仿真学报, 2022, 34(1): 53-61.

Han Beibei, Wei Yingmei, Fang Yujie, Wan Shanshan. Visual Analysis of Cross-domain Association of Time-series Data[J]. Journal of System Simulation, 2022, 34(1): 53-61.

参考文献

[1] 陈为, 沈则潜, 陶煜波. 数据可视化[M]. 2版. 北京: 电子工业出版社, 2019.
Chen Wei, Shen Zeqian, Tao Yubo.Data Visualization[M]. 2nd ed. Beijing: Publishing House of Electronics Industry, 2019.
[2] Abdullah K, Lee C, Conti G, et al.Visualizing network Data for Intrusion Detection[C]//Information Assurance Workshop, 2005, IAW'05, Proceedings from the Sixth Annual IEEE SMC. Piscataway, NJ USA: IEEE, 2005: 100-108.
[3] Havre S, Hetzler B, Nowell L.ThemeRiver: Visualizing theme changes over time[C]// Information Visualization, 2000, InfoVis 2000, IEEE Symposium on. USA: IEEE, 2000: 115-123.
[4] Lee J.Exploring Global Terrorism Data: A Web-based Visualization of Temporal Data[J]. Crossroads (S1825-7208), 2008, 15(2): 7-14.
[5] Wattenberg M. Baby Names, Visualization, and Social Data Analysis[C]//IEEE Information Visualization. USA: IEEE CS Press, 2005: 1.
[6] Wattenberg M, Viégas F.Beautiful History: Visualizing Wikipedia[M]. Beautiful Visualization: Looking at Data Through the Eyes of Experts. America: O'Reilly Media, 2010: 175-192.
[7] Liu S X, Zhou M X, Pan S M, et al.TIARA: Interactive, Topic-Based Visual Text Summarization and Analysis[J]. ACM Transactions on Intelligent Systems and Technology (S2157-6904), 2012, 3(2): 25.
[8] Hashimoto Y, Matsushita R.Heat Map Scope Technique for Stacked Time-series Data Visualization[C]// 16th International Conference on Information Visualisation, Piscataway, NJ USA: IEEE, 2012.
[9] 甄远刚, 陈谊, 刘莹, 等. 一种基于ThemeRiver模型的非连续层次数据可视化方法[J]. 系统仿真学报, 2015, 27(10): 2460-2466, 2474.
Zhen Yuangang, Chen Yi, Liu Ying, et al.Discontinuous Hierarchical Data Visualization Method Based on ThemeRiver Model[J]. Journal of System Simulation, 2015, 27(10): 2460-2466, 2474.
[10] 赵颖, 樊晓平, 周芳芳, 等. 多源网络安全数据时序可视分析方法研究[J]. 小型微型计算机系统, 2014(4): 906-910.
Zhao Ying, Fan Xiaoping, Zhou Fangfang, et al.Visualization of Multi-source Network Security Data Based on Stacked Stream Graph[J]. Journal of Chinese Computer Systems, 2014(4): 906-910.
[11] 陈馨菂, 李天瑞, 杨欢欢. 基于时间序列数据的交互式主题河流可视化[J]. 山东大学学报(工学版), 2019, 49(4): 29-35, 43.
Chen Xindi, Li Tianrui, Yang Huanhuan.Visualization of Interactive ThemeRiver Based on Time-series Data[J]. Journal of Shandong University (Engineering Science), 2019, 49(4): 29-35, 43.
[12] 北京大学可视化与可视化实验室. 新型冠状病毒肺炎疫情可视分析系列[EB/OL]. [2020-02-21]. http://vis.pku. edu. cn/ncov/home.html.
PKU Visualization and Visual Analytics Group. COVID-19 Visual Analysis Series. [EB/OL]. [2020-02-21]. http://vis.pku.edu.cn/ncov/home.html.
[13] 沈婕, 杜毅贤, 钟琳颖, 等. 新冠肺炎疫情下的多尺度舆情分析及动态可视化[EB/OL] [2020-04-23]. https://tianchi.aliyun.com/forum/postDetail?spm=5176.12281976.0.0.349f56b6nQvKLL&postId=104506.
Shen Jie, Du Yixian, Zhong Linying, et al.Multi-scale public opinion analysis and dynamic visualization under COVID-19 epidemic[EB/OL] [2020-04-23]. https://tianchi.aliyun.com/forum/postDetail?spm=5176.12281976.0.0.349f56b6nQvKLL&postId=104506.
[14] Sips M, Witt C, Rawald T, et al.Torwards Visual Analytics for the Exploration of Large Sets of Time Series[M]. Recurrence Plots and Their Quantifications: Expanding Horizons. Springer International Publishing, 2016.
[15] Fujiwara T, Shilpika, Sakamoto N, et al.A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction [EB/OL]. [2020-08-17].https://arxiv.org/abs/2008.01645.
[16] Steed C A, Halsey W, Dehoff R, et al.Falcon: Visual Analysis of Large, Irregularly Sampled, and Multivariate Time Series Data in Additive Manufacturing[J]. Computers & Graphics (S0097-8493), 2017, 63: 50-64.
[17] Urquiaga R R, Valdivia A M C, Zapana R A. A Visual Analytics Approach for Exploration of High-Dimensional Time Series Based on Neighbor-joining Tree[C]// 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). Bilbao, 2017: 325-330.
[18] Zhang D, Zhu L, Wang C, et al.TimeSpiral, an Enhanced Interactive Visual System for Time Series Data[C]// International Conference on Information Management. Piscataway, NJ USA: IEEE, 2016.
[19] Mohammed A, Alqahtani A, Jones M W, et al.Clustering and Classification for Time Series Data in Visual Analytics: A Survey[J]. IEEE Access (S2169-3536), 2019, 7(1): 181314-181338.
[20] Ko S, Cho I, Afzal S, et al.A Survey on Visual Analysis Approaches for Financial Data[C]// 2016 Eurographics Conference on Visualization. Piscataway, NJ USA: Comput. Graph. Forum 35, 2016: 599-617.
[21] 周志光, 孙畅, 乐丹丹, 等. 多维时空数据协同可视分析方法[J]. 计算机辅助设计与图形学学报, 2017(12): 2245-2255.
Zhou Zhiguang, Sun Chang, Le Dandan, et al.Collaborative Visual Analytics of Multi-dimensional and Spatio-temporal Data[J]. Journal of Computer-Aided Design & Computer Graphics, 2017(12): 2245-2255.
[22] 赵颖, 王权, 黄叶子, 等. 多视图合作的网络流量时序数据可视分析[J]. 软件学报, 2016, 27(5): 1188-1198.
Zhao Ying, Wang Quan, Huang Yezi, et al.Collaborative Visual Analytics for Network Traffic Time-Series Data with Multiple Views[J]. Journal of Software, 2016, 27(5): 1188-1198.

时序数据跨域关联可视分析

Visual Analysis of Cross-domain Association of Time-series Data

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