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Segmented Time-Series Plot: A New Design Technique for Visualization of Industrial Data

  • Tian Lei
  • Nan NiEmail author
  • Ken Chen
  • Xin He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10904)

Abstract

Time-series plots have been widely used in the fields of data analysis and data mining because of its good visual characteristics. However, when researching and analyzing the massive data formed in industrial, some shortcomings of the traditional time-series plot make the visualization of big data ineffective, which is not conducive to data analysis and mining.

In this paper, the traditional time-series graph is improved and a segmented time-series plot that can be used for massive industrial data analysis is proposed. In addition, this paper describes in detail the steps of making the segmented time-series plot. The method can reduce the information overload and the interface issues by limiting the amount of information presented.

Keywords

Data visualization Design technique Industrial data Segmentation method 

References

  1. 1.
    Akpan, I.J., Shanker, M.: The confirmed realities and myths about the benefits and costs of 3D visualization and virtual reality in discrete event modeling and simulation: a descriptive meta-analysis of evidence from research and practice. Comput. Ind. Eng. 112, 197–211 (2017)CrossRefGoogle Scholar
  2. 2.
    Jiang, X.F., Li, J.Y.: Research on the digital industrial design system integrated with product data management system. Adv. Mater. Res. 102, 106–110 (2010)CrossRefGoogle Scholar
  3. 3.
    王孜弘: 新常态下中美综合经济实力对比——基于国内生产总值的分析. 美国研究 30(05), 31–49+6 2016. (in Chinese)Google Scholar
  4. 4.
    Gil, D., Song, I.-Y.: Modeling and management of big data: challenges and opportunities. Future Gener. Comput. Syst. 63, 96–99 (2016)CrossRefGoogle Scholar
  5. 5.
    Lu, Z., Zhangm, Q.: Clustering by data competition. Sci. China (Inf. Sci.) 56(01), 65–77 (2013)MathSciNetGoogle Scholar
  6. 6.
    李晓明: 采用Excel制作数据分段统计表的研究. 电脑知识与技术 10(23), 5572–5579 (2014). (in Chinese)Google Scholar
  7. 7.
    Lin, W.-M., Gow, H.-J., Tsay, M.-T.: A partition approach algorithm for nonconvex economic dispatch. Int. J. Electr. Power Energy Syst. 29(5), 432–438 (2006)CrossRefGoogle Scholar
  8. 8.
    Stoica, P., Soderstrom, T., Ahlen, A., Solbrand, G.: On the convergence of pseudo-linear regression algorithms. Int. J. Control 41(6), 1429–1444 (1985)CrossRefGoogle Scholar
  9. 9.
    田野, 张忠能: 改进的基于重要点的时间序列数据分段方法. 微型电脑应用 28(2), 48–51 (2012). (in Chinese)Google Scholar
  10. 10.
    喻高瞻, 彭宏, 胡劲松, 等: 时间序列数据的分段线性表示. 计算机应用与软件 24(12), 17–18 (2007). (in Chinese)Google Scholar
  11. 11.
    孙焕良,邱邦华,魏溯华. 一种优化的自底向上时间序列分段算法. 沈阳建筑大学学报(自然科学版) (06), 1049–1052 (2007). (in Chinese). Assessed 12 Oct 2017Google Scholar
  12. 12.
    Evergreen, S., Metzner, C.: Design principles for data visualization in evaluation. New Dir. Eval. 2013(140), 5–20 (2013)CrossRefGoogle Scholar
  13. 13.
    Cockburn, A., Karlson, A., Bederson, B.B.: A review of overview + detail, zooming, and focus + context interfaces. ACM Comput. Surv. (CSUR) 41(1), 2 (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

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