Abstract：Phased time series data is common in daily life. It describes an event that contains a number of state transitions. Each state has a time attribute, and there are multiple paths between state transitions. Aiming at the problem that the existing visualization techniques are not sufficient in visualizing the transition of each phase or the time variation of paths between states, a novel visualization model based on spiral graph was proposed. In the proposed model, each state was represented by a circle and the states of an event were represented by a set of concentric circles, and the reachable paths between neighboring states were represented by spirals. The start point of each spiral depended on its start time and the start states, and the end point of each spiral depended on its end time and the end states. To solve the overlapping problem caused by large amount of paths, the transparency adjustment algorithm based on long-tailed function was applied on the paths. The transparency of each path was assigned according to the number of intersections of this path and other paths. Flexible interactive facilities such as path filtering, highlighting, bomb box and zooming were provided to support efficient data exploration. The proposed model was implemented on China railway data, the experimental result shows that the model can effectively display trains of any running duration in limited space and is able to reduce the chaos caused by paths overlapping when confronted with large amount of trains as well as keep the information of trains and provide decision support for the user route choice, which validates the effectiveness of the proposed model in visualizing phased time series data.
杨欢欢, 李天瑞, 陈馨菂. 基于螺旋图的时间序列数据可视化[J]. 计算机应用, 2017, 37(9): 2443-2448.
YANG Huanhuan, LI Tianrui, CHEN Xindi. Visualization of time series data based on spiral graph. Journal of Computer Applications, 2017, 37(9): 2443-2448.
[1] LEI H, XIA J, GUO F, et al. Visual exploration of latent ranking evolutions in time series[J]. Journal of Visualization, 2016, 19(4):783-795. [2] 姜婷婷,肖卫东,张翀,等.基于桑基图的时间序列文本可视化方法[J]. 计算机应用研究,2016,33(9):2683-2687.(JIANG T T, XIAO W D, ZHANG C, et al. Text visualization method for time series based on Sankey diagram[J]. Application Research of Computers, 2016, 33(9):2683-2687.) [3] BOUALI F, DEVAUX S, VENTURINI G. Visual mining of time series using a tubular visualization[J]. The Visual Computer, 2016, 32(1):15-30. [4] WEBER M, ALEXA M, MVLLER W. Visualizing time-series on spirals[C]//Proceedings of the 2001 IEEE Symposium on Information Visualization. Washington, DC:IEEE Computer Society, 2001:7-14. [5] CARLIS J V, KONSTAN J A. Interactive visualization of serial periodic data[C]//Proceedings of the 11th Annual ACM Symposium on User Interface Software and Technology. New York:ACM, 1998:29-38. [6] CHENG S, JIANG Z, QI Q, et al. The polar parallel coordinates method for time-series data visualization[C]//Proceedings of 20124th International Conference on Computational and Information Sciences. Washington, DC:IEEE Computer Society, 2012:179-182. [7] HEWAGAMAGE K P, HIRAKAWA M, ICHIKAWA T. Interactive visualization of spatiotemporal patterns using spirals on a geographical map[C]//Proceedings of the 1999 IEEE Symposium on Visual languages. Washington, DC:IEEE Computer Society, 1999:296-303. [8] DRAGICEVIC P, HUOT S. SpiraClock:a continuous and non-intrusive display for upcoming events[C]//Proceedings of the CHI'02 Extended Abstracts on Human Factors in Computing Systems. New York:ACM, 2002:604-605. [9] TOMINSKI C, SCHUMANN H. Enhanced interactive spiral display[EB/OL].[2016-11-25]. http://www.informatik.uni-rostock.de/~schumann/papers/2008%2B/tominski_spiral_display.pdf. [10] BUELENS B. Visual circular analysis of 266 years of sunspot counts[J]. Big Data, 2016, 4(2):89-96. [11] WALKER J, BORGO R, JONES M W. TimeNotes:a study on effective chart visualization and interaction techniques for time-series data[J]. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):549-558.
2017, Vol. 37 
