The Review of Visual Analysis Methods of Multi-modal Spatio-temporal Big Data

doi:10.11947/j.AGCS.2017.20170286

Abstract

Abstract: The visual analysis of spatio-temporal big data is not only the state-of-art research direction of both big data analysis and data visualization, but also the core module of pan-spatial information system. This paper reviews existing visual analysis methods at three levels:descriptive visual analysis, explanatory visual analysis and exploratory visual analysis, focusing on spatio-temporal big data's characteristics of multi-source, multi-granularity, multi-modal and complex association.The technical difficulties and development tendencies of multi-modal feature selection, innovative human-computer interaction analysis and exploratory visual reasoning in the visual analysis of spatio-temporal big data were discussed. Research shows that the study of descriptive visual analysis for data visualizationis is relatively mature.The explanatory visual analysis has become the focus of the big data analysis, which is mainly based on interactive data mining in a visual environment to diagnose implicit reason of problem. And the exploratory visual analysis method needs a major break-through.

Key words: spatio-temporal big data, visual analysis, pan-spatial information system, multi-modal data

CLC Number:

P208

ZHU Qing, FU Xiao. The Review of Visual Analysis Methods of Multi-modal Spatio-temporal Big Data[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1672-1677.

References

[1] HEY T, TANSLEY S, TOLLE K. The Fourth Paradigm:Data-intensive Scientific Discovery[M]. Redmond, Washington:Microsoft Research, 2009.
[2] ANDRIENKO G, ANDRIENKO N, DEMSAR U, et al. Space, Time and Visual Analytics[J]. International Journal of Geographical Information Science, 2010, 24(10):1577-1600.
[3] KEIM D A, MANSMANN F, THOMAS J. Visual Analytics:How Much Visualization and How Much Analytics?[J]. ACM SIGKDD Explorations Newsletter, 2009, 11(2):5-8.
[4] KEIM D A, MANSMANN F, SCHNEIDEWIND J, et al. Visual Analytics:Scope and Challenges[M]//SIMOFF S J, BÖHLEN M H, MAZEIKA A. Visual Data Mining. Berlin Heidelberg:Springer-Verlag, 2008.
[5] MENG L, ZIPF A, WINTER S. Map-based Mobile Services[M]. Berlin:Springer, 2005.
[6] BERTONE A, BURGHARDT D. A Survey on Visual Analytics for the Spatio-temporal Exploration of Microblogging Content[J]. Journal of Geovisualization and Spatial Analysis, 2017, 1(1-2):2.
[7] 周成虎. 全空间地理信息系统展望[J]. 地理科学进展, 2015, 34(2):129-131. ZHOU Chenghu. Prospects on Pan-spatial Information System[J]. Progress in Geography, 2015, 34(2):129-131.
[8] 华一新. 全空间信息系统的核心问题和关键技术[J]. 测绘科学技术学报, 2016, 33(4):331-335. HUA Yixin. The Core Problems and Key Technologies of Pan-spatial Information System[J]. Journal of Geomatics Science and Technology, 2016, 33(4):331-335.
[9] 李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4):379-384. LI Deren. Towards Geo-spatial Information Science in Big Data Era[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4):379-384.
[10] 章毅, 郭泉, 王建勇. 大数据分析的神经网络方法[J]. 工程科学与技术, 2017, 49(1):9-18. ZHANG Yi, GUO Quan. WANG Jianyong. Big Data Analysis Using Neural Networks[J]. Advanced Engineering Sciences, 2017, 49(1):9-18.
[11] MCGURK H, MACDONALD J. Hearing Lips and Seeing Voices[J]. Nature, 1976, 246(5588):746-748.
[12] ZHENG Yu. Methodologies for Cross-Domain Data Fusion:An Overview[J]. IEEE Transactions on Big Data, 2015, 1(1):16-34.
[13] LAHAT D, ADALI T, JUTTEN C. Multimodal Data Fusion:An Overview of Methods, Challenges, and Prospects[J]. Proceedings of the IEEE, 2015, 103(9):1449-1477.
[14] KHALEGHI B, KHAMIS A, KARRAY F O, et al. Multisensor Data Fusion:A Review of the State-of-the-Art[J]. Information Fusion, 2013, 14(1):28-44.
[15] TURK M. Multimodal Interaction:A Review[J]. Pattern Recognition Letters, 2014(36):189-195.
[16] 马昱欣, 曹震东, 陈为. 可视化驱动的交互式数据挖掘方法综述[J]. 计算机辅助设计与图形学学报, 2016, 28(1):1-8. MA Yuxin, CAO Zhendong, CHEN Wei. A Survey of Visualization-driven Interactive Data Mining Approaches[J]. Journal of Computer-Aided Design and Computer Graphics, 2016, 28(1):1-8.
[17] DE OLIVEIRA M C F, LEVKOWITZ H. From Visual Data Exploration to Visual Data Mining:A Survey[J]. IEEE Transactions on Visualization and Computer Graphics, 2003, 9(3):378-394.
[18] 陈为. 大数据可视化与可视分析[J]. 金融电子化, 2015(11):62-65. CHEN Wei. Big Data Visualization and Visual Analysis[J]. Financial Computerizing, 2015(11):62-65.
[19] ZHUANG Yueting, WU Fei, CHEN Chun, et al. Challenges and Opportunities:From Big Data to Knowledge in AI 2.0[J]. Frontiers of Information Technology and Electronic Engineering, 2017, 18(1):3-14.
[20] 朱庆. 三维GIS及其在智慧城市中的应用[J]. 地球信息科学学报, 2014, 16(2):151-157. ZHU Qing. Full Three-Dimensional GIS and Its Key Roles in Smart City[J]. Journal of Geo-Information Science, 2014, 16(2):151-157.
[21] AIGNER W, MIKSCH S, MÜLLER W, et al. Visualizing Time-Oriented Data:A Systematic View[J]. Computers and Graphics, 2007, 31(3):401-409.
[22] MÜLLER W, SCHUMANN H. Visualization Methods for Time-Dependent Data:An Overview[C]//Proceedings of 2003 Winter Simulation Conference. New Orleans, LA, USA:IEEE, 2003.
[23] 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. San Francisco, California, USA:ACM, 1998:29-38.
[24] TOMINSKI C, ABELLO J, SCHUMANN H. Axes-based Visualizations with Radial Layouts[C]//Proceedings of the 2004 ACM Symposium on Applied Computing. Nicosia, Cyprus:ACM, 2004:1242-1247.
[25] DEMŠAR U, VIRRANTAUS K. Space-time Density of Trajectories:Exploring Spatio-temporal Patterns in Movement Data[J]. International Journal of Geographical Information Science, 2010, 24(10):1527-1542.
[26] ANDRIENKO G, ANDRIENKO N. Visual Analytics for Geographic Analysis, Exemplified by Different Types of Movement Data[M]//POPOVICH V V, CLARAMUNT C, SCHRENK M, et al. Information Fusion and Geographic Information Systems. Berlin:Springer, 2009.
[27] 陈为, 朱标, 张宏鑫. BN-Mapping:基于贝叶斯网络的地理空间数据可视分析[J]. 计算机学报, 2016, 39(7):1281-1293. CHEN Wei, ZHU Biao, ZHANG Hongxin. BN-Mapping:Visual Analysis of Geospatial Data with Bayesian Network[J]. Chinese Journal of Computers, 2016, 39(7):1281-1293.
[28] COSSALTER M, MENGSHOEL O, SELKER T. Visualizing and Understanding Large-scale Bayesian Networks[J]. Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI Workshop on Scalable Integration of Analytics and Visualization. San Francisco:AAAI, 2011.
[29] 孙扬, 蒋远翔, 赵翔, 等. 网络可视化研究综述[J]. 计算机科学, 2010, 37(2):12-18, 30. SUN Yang, JIANG Yuanxiang, ZHAO Xiang, et al. Survey on the Research of Network Visualization[J]. Computer Science, 2010, 37(2):12-18, 30.
[30] DIAZ I, CUADRADO A A, PÉREZ D, et al. Interactive Dimensionality Reduction for Visual Analytics[C]//Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium, 2014.
[31] BRUZZESE D, DAVINO C. Visual Mining of Association Rules[M]//SIMOFF S J, BÖHLEN M H, MAZEIKA A. Visual Data Mining. Berlin, Heidelberg:Springer, 2008:103-122.
[32] HOFMANN H, SIEBES A P J M, WILHELM A F X. Visualizing Association Rules with Interactive Mosaic Plots[C]//Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, Massachusetts, USA:ACM, 2000:227-235.
[33] KANDOGAN E. Visualizing Multi-dimensional Clusters, Trends, and Outliers Using Star Coordinates[C]//Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California:ACM, 2001:107-116.
[34] CHEN Keke, LIU Ling. VISTA:Validating and Refining Clusters Via Visualization[J]. Information Visualization. 2004, 3(4):257-270.
[35] ZHANG Kebing, ORGUN M A, ZHANG Kang, et al. Hypothesis Oriented Cluster Analysis in Data Mining by Visualization[C]//Proceedings of the Working Conference on Advanced Visual Interfaces. Venezia, Italy:ACM, 2006:254-257.
[36] ROBINSON A C, DEMŠAR U, MOORE A B, et al. Geospatial Big Data and Cartography:Research Challenges and Opportunities for Making Maps that Matter[J]. International Journal of Cartography, 2017:1-29. DOI:10.1080/23729333.2016.1278151.