SRDVis: A Visual Analysis System for Record Data of Internet Cafe

doi:10.11871/jfdc.issn.2096-742X.2021.04.006

Abstract

Abstract:

[Objective] In order to mine user characteristics, build behavioral models, and discover co-occurring groups from the spatio-temporal data of individual user behaviors, we design and implement a visual analysis system SRDVis based on the public internet cafe record data. [Methods] On the basis of the inherent attributes and characteristics of this type of data, we have developed a visualization system that can meet the requirements of data analysis tasks in this field through processing, statistics and analysis of the raw data, combined with methods and technologies such as visualization design, element correlation, and human-computer interaction. [Results] Aiming at the characteristics of user behavior record data in internet cafes, we design various visualization views that can display data characteristics from different perspectives. These views meet the needs of business supervisors for the management and analysis of data in this field. [Conclusions] We have verified the effectiveness of the analysis method and the system usability through two case studies based on real business scenarios. This system can realize visual analysis of such spatio-temporal data. In addition, the system also has a certain degree of scalability and can be used in a variety of business areas.

Key words: spatio-temporal data, visual analysis, records of internet cafe, co-occurrence groups

ZHAO Fan,MA Xiaodong,REN Pengkun. SRDVis: A Visual Analysis System for Record Data of Internet Cafe[J]. Frontiers of Data and Computing, 2021, 3(4): 70-80.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

References 21

[1]	陈为, 沈则潜, 陶煜波. 数据可视化[M]. 北京: 电子工业出版社, 2013: 456-476.
[2]	Fua Y H, Ward M O, Rundensteiner E A. Hierarchical parallel coordinates for exploration of large datasets[C] //Proceedings of the IEEE Conference on Visualization. Los Alamitos: IEEE Computer Society Press, 1999: 43-50.
[3]	Zhou H, Yuan X R, Qu H M, et al. Visual clustering in parallel coordinates[J]. Computer Graphics Forum, 2008,27(3):1047-1054. doi: 10.1111/cgf.2008.27.issue-3
[4]	Tatu A, Albuquerque G, Eisemann M, et al. Automated analytical methods to support visual exploration of high-dimensional data[J]. IEEE Transactions on Visualization and Computer Graphics, 2011,17(5):584-597. doi: 10.1109/TVCG.2010.242
[5]	Ward M O. XmdvTool: integrating multiple methods for visualizing multivariate data[C] //Proceedings of the IEEE Conference on Visualization. Los Alamitos: IEEE Computer Society Press, 1994: 326-333.
[6]	Aigner W, Miksch S, Schumann H, et al. Visualization of time-oriented data[M]. Heidelberg: Springer, 2011: 59-99.
[7]	Weber M, Alexa M, Muller W. Visualizing time-series on spirals[C] //Proceedings of the IEEE Conference on Information Visualization. Los Alamitos: IEEE Computer Society Press, 2001: 7-13.
[8]	McLachlan P, Munzner T, Koutsofios E, et al. LiveRAC: interactive visual exploration of system management time-series data[C] //Proceedings of the SIGCHI Con-ference on Human Factors in Computing Systems. New York: ACM Press, 2008: 1483-1492.
[9]	Elmqvist N, Riche Y, Henry-Riche N, et al. Mélange: space folding for visual exploration[J]. IEEE Transactions on Visualization and Computer Graphics, 2010,16(3):468-483. doi: 10.1109/TVCG.2009.86 pmid: 20224141
[10]	Javed W, Elmqvist N. Stack zooming for multi-focus interaction in time-series data visualization[C] //Proce-edings of the IEEE Pacific Visualization Symposium. Los Alamitos: IEEE Computer Society Press, 2010: 33-40.
[11]	Gruendl H, Riehmann P, Pausch Y, et al. Time-series plots integrated in parallel-coordinates displays[J]. Computer Graphics Forum, 2016,35(3):321-330. doi: 10.1111/cgf.12908
[12]	Martin A R, Ward M O. High dimensional brushing for interactive exploration of multivariate data[C] //Proce-edings of the 6th IEEE Conference on Visualization. Los Alamitos: IEEE Computer Society Press, 1995: 271.
[13]	Yang J, Peng W, Ward M O, et al. Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets[C] //Proceedings of the IEEE Symposium on Information Visualization. Los Ala-mitos: IEEE Computer Society Press, 2003: 105-112.
[14]	Furnas G W. A fisheye follow-up: further reflections on focus+context[C]//Proceedings of the SIGCHI Confer-ence on Human Factors in Computing Systems. New York: ACM Press, 2006: 999-1008.
[15]	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.
[16]	陈谊, 蔡进峰, 石耀斌, 等. 基于平行坐标的多视图协同可视分析方法[J]. 系统仿真学报, 2013,25(1):81-86.
[17]	Cornec O, Vuillemot R. Visualizing the scale of world economies[C]//VisWeek 2015 Electronic Conference Proceedings-Poster. 2015.
[18]	Chen H, Chen W, Mei H, et al. Visual Abstraction and Exploration of Multi-class Scatterplots[J]. IEEE Transa-ctions on Visualization & Computer Graphics, 2014,20(12):1683-92.
[19]	Guo D, Zhu X. Origin-destination flow data smoothing and mapping[J]. IEEE Transactions on Visualization and Computer Graphics, 2014,20(12):2043-2052. doi: 10.1109/TVCG.2945
[20]	Collins C, Penn G, Carpendale S. Bubble sets: revealing set relations with isocontours over existing visualizations[J]. IEEE Transactions on Visualization & Computer Graphics, 2009,15(6):1009-1016.
[21]	Yuan X, Xiao H, Guo H, et al. Scalable Multi-variate Analytics of Seismic and Satellite-based Observational Data[J]. IEEE Transactions on Visualization & Computer Graphics, 2010,16(6):1413.