[1]方思桐,汪隶鋆,高 敏,等.交互式电力数据可视化与分析[J].南京师范大学学报(自然科学版),2019,42(03):96-16.[doi:10.3969/j.issn.1001-4616.2019.03.013]
　Fang Sitong,Wang Lijun,Gao Min,et al.Interactive Power Data Visualization and Analytics[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):96-16.[doi:10.3969/j.issn.1001-4616.2019.03.013]
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交互式电力数据可视化与分析()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年03期
页码:
96-16
栏目:
·全国机器学习会议论文专栏·
出版日期:
2019-09-30

文章信息/Info

Title:
Interactive Power Data Visualization and Analytics
文章编号:
1001-4616(2019)03-0096-11
作者:
方思桐; 汪隶鋆; 高 敏; 钱荣涛; 陈雪羿; 申丽铭; 张力丹; 刘日晨; 王 琼
南京师范大学计算机科学与技术学院,江苏 南京 210023
Author(s):
Fang Sitong; Wang Lijun; Gao Min; Qian Rongtao; Chen Xueyi; Shen Liming; Zhang Lidan; Liu Richeng; Wang Qiong
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
关键词:
电力数据可视化; 可视分析; 交互式探索; 比较可视化
Keywords:
power data visualization; visual analytics; interactive exploration; comparative visualization
分类号:
TP311
DOI:
10.3969/j.issn.1001-4616.2019.03.013
文献标志码:
A
摘要:
并排法是传统的比较可视化中较为经典的方法,即将多个集合成员或不同的结果在一个显示空间中并排显示. 本文所使用的是具有时序性的异构、高维数据,包括全国GDP数据及中国西南某省43个企业或商场的用电数据,以及天气数据. 考虑到数据量庞大,并排可视化方法不具有空间协调性与视觉直观性,因此,本文使用重叠法与显式编码的比较可视化技术,使用户可自由选择日期,将多天的采样点数据重叠在一个显示空间中,避免了视觉混乱,使数据分析更加简洁高效.
Abstract:
Power data have the characteristics of time-sequence,heterogeneity and high-dimension,while the parallel visualization method universally used in the analysis of power data lacks of both spatial coordination and visual intuition. In view of these properties and limitations of traditional methods,an interactive visualization method is designed for power data comparison. This method makes use of the technology of superposition and explicit encoding comparison visualization to compare and analyze multi-day sample data in a carefully designed circular layout,avoiding visual confusion,improving the effect of multi-view comparison,making the process of data analysis more concise and efficient. In the experimental part,taking the data of GDP,electricity consumption and weather data of 43 enterprises or shopping malls in a province of China as examples,this paper validates the method we presented and compares it with the traditional method. The result indicates that the proposed method is more intuitive and flexible in data analysis.

参考文献/References:

[1] KEIM D A. Information visualization and visual data mining[J]. IEEE transactions on visualization and computer graphics,2002,8(1):1-8.
[2]舒清雅,刘日晨,洪帆,等. 集合模拟可视化进展[J]. 软件学报,2018,29(2):506-523.
[3]GLEICHER M,ALBERS D,WALKER R,et al. Visual comparison for information visualization[J]. Information visualization,2011,10(4):289-309.
[4]DODGE Y. The concise encyclopedia of statistics[M]. USA:Springer Science & Business Media,2008.
[5]KOBAYASHI I. Toward text based information processing:with an example of natural language modeling of a line chart[C]//IEEE SMC’99 Conference Proceedings. 1999 IEEE International Conference on Systems,Man,and Cybernetics(Cat. No. 99CH37028). Tokyo,Japan:IEEE,1999.
[6]DEMIR I,DICK C,WESTERMANN R. Multi-charts for comparative 3d ensemble visualization[J]. IEEE transactions on visualization and computer graphics,2014,20(12):2694-2703.
[7]RYAN G,MOSCA A,CHANG R,et al. At a Glance:pixel approximate entropy as a measure of line chart complexity[J]. IEEE transactions on visualization and computer graphics,2019,25(1):872-881.
[8]K?THUR P,WITT C,SIPS M,et al. Visual analytics for correlation-based comparison of time series ensembles[J]. Computer graphics forum,2015,34(3):411-420.
[9]BISWAS A,LIN G,LIU X,et al. Visualization of time-varying weather ensembles across multiple resolutions[J]. IEEE transactions on visualization and computer graphics,2017,23(1):841-850.
[10]BOSTOCK M,OGIEVETSKY V,HEER J. D3 data-driven documents[J]. IEEE transactions on visualization and computer graphics,2011,17(12):2301-2309.
[11]陈为,张嵩,鲁爱东. 数据可视化的基本原理与方法[M]. 北京:科学出版社,2013.
[12]CARD S K,MACKINLAY J D,SHNEIDERMAN B. Readings in information visualization:using vision to think[M]//Readings in information visualization. San Francisco:Morgan Kaufmann,1999.
[13]任磊,杜一,马帅,等. 大数据可视分析综述[J]. 软件学报,2014(9):1909-1936.
[14]张力丹. 用电数据可视分析的应用研究[D]. 南京:南京师范大学,2018.
[15]WANG P,RAO L,LIU X,et al. D-pro:dynamic data center operations with demand-responsive electricity prices in smart grid[J]. IEEE transactions on smart grid,2012,3(4):1743-1754.
[16]郑斌祥,席裕庚,杜秀华. 基于离群指数的时序数据离群挖掘[J]. 自动化学报,2004,30(1):70-77.
[17]SHNEIDERMAN B. The eyes have it:a task by data type taxonomy for information visualizations[C]//Proc IEEE Symposium on Visual Languages. Boulder,Colorado:IEEE Computer Society,1996.
[18]KRZYWINSKI M,SCHEIN J,BIROLl I,et al. Circos:An information aesthetic for comparative genomics[J]. Genome research,2009,19(9):1639-1645.
[19]FRIENDLY M,DENIS D. The early origins and development of the scatterplot[J]. Journal of the history of the behavioral sciences,2005,41(2):103-130.
[20]DOLEISCH H,GASSER M,HAUSER H. Interactive feature specification for focus+context visualization of complex simulation data[C]//Nordic Conference on Human-computer Interaction:Extending Boundaries. Reykjavik,Iceland:ACM,2010.
[21]HEER J,BOSTOCK M. Declarative language design for interactive visualization[J]. IEEE transactions on visualization and computer graphics,2010,16(6):1149-1156.
[22]张卓,宣蕾,郝树勇. 可视化技术研究与比较[J]. 现代电子技术,2010,33(17):133-138.[23]LIU R,CHEN S,JI G,et al. Interactive stratigraphic structure visualization for seismic data[J]. Journal of vsual languages and computing,2018,48(1):81-90.
[24]LIU R,GUO H,YUAN X. Seismic structure extraction based on multi-scale sensitivity analysis[J]. Journal of visualization,2014,17(3):157-166.
[25]LIU R,GUO H,YUAN X. A bottom-up scheme for user-defined feature comparison in ensemble data[C]//ACM SIGGRAPH Asia Symposium on Visualization in High Performance Computing. Kobe,Japan:ACM,2015.
[26]LIU R,GUO H,ZHANG J,et al. Comparative visualization of vector field ensembles based on longest common subsequence[C]//IEEE Pacific Visualization Symposium. Taipei,China:IEEE,2016.
[27]LIU R,GUO H,YUAN X. User-defined feature comparison for vector field ensembles[J]. Journal of visualization,2017,20(2):217-229.
[28]GAO M,WANG L,JIA J,et al. Interactive geological visualization based on quadratic-surface distance query[J]. Journal of electronic imaging,2019,28(2):1-10.
[29]GAO M,XIANG Y,WANG L,et al. Histogram-based nonlinear transfer function edit and fusion[C]//The 10th International Conference on Image and Graphics. Beijing,China:CSIG,2019.

备注/Memo

备注/Memo:
收稿日期:2019-07-05.基金项目:国家自然科学基金(61702271、41471371、61702270). 通讯联系人:王琼,副教授,研究方向:计算机图形学. E-mail:329789995@qq.com
更新日期/Last Update: 2019-09-30