Development status and trend of data analysis technology and tools

Abstract

Abstract: Data analysis technology is an important part of big data technology. It plays a key role in the deep use of data and the release of value. At present, global analysis technology is developing rapidly, and key technologies in various fields of data analysis have made breakthroughs. Starting from the three data analysis areas of data visualization, automated data modeling, and situational awareness, this article introduces the current status of data visualization, automated data modeling, and situational awareness processing and analysis, summarizes related tools and applications, and discusses the development trend of data analysis technology.

Key words: data analysis, visualization, data modeling, situational awareness

LI Yufei. Development status and trend of data analysis technology and tools[J]. Information and Communications Technology and Policy, 2020, 46(4): 23-30.

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URL: http://ictp.caict.ac.cn/EN/Y2020/V46/I4/23

References 29

[1]	高志鹏, 牛琨, 刘杰. 面向大数据的分析技术[J]. 北京邮电大学学报, 2015,38(3):1-12.
[2]	维克托·迈尔·舍恩伯格, 肯尼斯·库克耶. 大数据时代[M]. 杭州: 浙江人民出版社, 2012.
[3]	陶雪娇, 胡晓峰, 刘洋. 大数据研究综述[J]. 系统仿真学报, 2013,8( 25) : 144-145.
[4]	程学旗, 靳小龙, 王元卓, 等. 大数据系统和分析技术综述[J]. 软件学报, 2014, 25(9) : 1889-1908.
[5]	孔微. 时空多维大数据可视化系统的设计与实现[D].北京邮电大学, 2019.
[6]	Ram VinayPandey, Stephan Pabinger, Albert Kriegner,Andreas Weinhäusel. DaMold: A data ‐ mining platform
	for variant annotation and visualization in molecular diagnostics research[J]. Human Mutation,2017,38(7).
[7]	Wu Yingbin, Guo Fuliang. Visualization technology based on the treatment of complicated data[J]. Computer& Digital Engineering, 2015, 43(11): 1985-1989.
[8]	Card SK, Mackinlay JD, Shneiderman B. Readings in information visualization: Using visionto think. San Francisco: Morgan-Kaufmann Publishers, 1999. 1-712.
[9]	任磊, 杜一, 马帅, 等. 大数据可视分析综述[J]. 软件学报, 2014,25(9):1909-1936.
[10]	左圆圆, 王媛媛, 蒋珊珊,等. 数据可视化分析综述[J]. 科技与创新, 2019(11):82-83.
[11]	Gantz J, Reinsel D. 2011 digital universe study:Extracting vaLue from chaos [M]. IDC Go-to-Market Services, 2011: 1-12.
[12]	王艺, 任淑霞. 医疗大数据可视化研究综述[J]. 计算机科学与探索, 2017,11(5):681-699.
[13]	唐家渝, 刘知远, 孙茂松. 文本可视化研究综述[J].计算机辅助设计与图形学学报, 2013,25(3):273-285.
[14]	唐翔. 新闻文本的可视化与可视分析[D]. 上海交通大学, 2018.
[15]	柏茂源, 代福平. 网络动态可视化的交互设计原则构建[J]. 包装工程, 2018,39(4):193-198.
[16]	王瑞松. 大数据环境下时空多维数据可视化研究[D]. 浙江大学,2016.
[17]	周志光, 石晨, 史林松, 等. 地理空间数据可视分析综述[J]. 计算机辅助设计与图形学学报, 2018,30(5):747-763.
[18]	姜枫, 许佳秋. 大数据可视化技术[M]. 北京:人民邮电出版社, 2019.
[19]	张平文, 鄂维南, 袁晓如, 等. 大数据分析与应用技术创新平台[J]. 大数据, 2018,4(4):86-93.
[20]	WENG Ziqiao. From conventional machine learning to autoML [ J ]. Journal of Physics: Conference Series, 2019,1207(1).
[21]	袁静. 国内外情景感知服务研究综述[J]. 国家图书馆学刊, 2018,27(3):39-47.
[22]	Dey A K. Providing architectural support for building context aware applications [ D ]. Atlanta, CA, USA:Georgia Institute of Technology, 2000.
[23]	童恩栋, 沈强, 雷君, 等. 物联网情景感知技术研究24] 李枫林, 陈德鑫. 社会情景感知计算及其关键技术研
究[J]	计算机科学, 2011,38(4):9-14+20.
[24]	李枫林, 陈德鑫. 社会情景感知计算及其关键技术研究[J]. 图书情报工作, 2016,60(9):139-146.
[25]	Shen Cong. Parallel volume rendering under multi-GPU environment[D]. Hangzhou: Zhejiang University, 2003.
[26]	Xie Shunhua. Research and implementation of streaming technology framework for distributed medial image[D].
	Hangzhou: Zhejiang University, 2013.
[27]	Xiao Yongfei. Research on 3D interactive visualization for medical data [ D ]. Harbin: Harbin Institute of Technology, 2010.
[28]	李恒, 沈华伟, 黄蔚, 等. 地理社会网络数据可视化分析研究综述[J]. 中文信息学报, 2018,32(10):11-18.
[29]	Adomavicius G, Tuzhilin A. Context -aware recommender systems [ M ]. Recommender Systems Handbook: a Complete Guide for Research Scientists and Practitioners,Springer, 2011: 217-25.