Abstract
In the large-scale power Internet of things, a large amount of data is generated due to its diversity. Data visualization technology is very important for people to capture the mathematical characteristics, rules and knowledge of data. People tend to get limited and less valuable information directly form large data when rely only on human-being’s cognition. Therefore, people need new means and technologies to help display these data more intuitively and effectively. Data visualization mainly aims at conveying and communicating information clearly and effectively in term of graphical display, which can make data more human-readable and intuitive. Multidimensional data visualization refers to the methods to project multidimensional data to two-dimensional plane. It has important applications in exploratory data analysis, and verification of clustering or classification problems. This paper mainly studies the data visualization algorithm and technology in large-scale power Internet of things. Specifically, the traditional Radviz algorithm is selected and improved. The improved radviz-t algorithm is designed and implemented, and the unknown information of data transmission is obtained by analyzing its visualization effect. Finally, the methods used to study fault detection ability of radviz-t algorithm are discussed in detail.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sharko, J., Grinstein, G., Marx, K.A.: Vectorized Radviz and its application to multiple cluster datasets. IEEE Trans. Vis. Comput. Graph. 14(6) (2008)
Novikova, E., Kotenko, I.: Visual analytics for detecting anomalous activity in mobile money transfer services. In: Teufel, S., Min, T.A., You, I., Weippl, E. (eds.) CD-ARES 2014. LNCS, vol. 8708, pp. 63–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10975-6_5
Lehmann, D.J., Theisel, H.: General projective maps for multidimensional data projection. In: Computer Graphics Forum, vol. 35, no. 2 (2016)
Daniels, K., Grinstein, G., Russell, A., Glidden, M.: Properties of normalized radial visualizations. Inform. Vis. 11(4), 273–300 (2012)
Ravichandran, S., Chandrasekar, R.K., Uluagac, A.S., Beyah, R.: A simple visualization and programming framework for wireless sensor networks: PROVIZ. Ad Hoc Netw. 53, 1–16 (2016)
Orsi, R.: Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map. Eval. Program Plann. 60, 277–283 (2016)
Keim, D.A., Mansmann, F., Schneidewind, J., Ziegler, H.: Challenges in visual data analysis. In: Tenth International Conference on Information Visualization, IV 2006, pp. 9–16. IEEE (2006)
Rao, T. R., Mitra, P., Bhatt, R., Goswami, A.: The big data system, components, tools, and technologies: a survey. Knowl. Inform. Syst. 1–81 (2018)
Li, P., Chen, Z., Yang, L.T., Zhang, Q., Deen, M.J.: Deep convolutional computation model for feature learning on big data in internet of things. IEEE Trans. Ind. Inform. 14(2), 790–798 (2018)
Senaratne, H., et al.: Urban mobility analysis with mobile network data: a visual analytics approach. IEEE Trans. Intell. Transp. Syst. 19(5), 1537–1546 (2018)
Zhou, F., Huang, W., Li, J., Huang, Y., Shi, Y., Zhao, Y.: Extending dimensions in radviz based on mean shift. In: 2015 IEEE Pacific Visualization Symposium (PacificVis), pp. 111–115. IEEE (2015)
Shi, L., Liao, Q., He, Y., Li, R., Striegel, A., Su, Z.: SAVE: Sensor anomaly visualization engine. In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 201–210. IEEE (2011)
Acknowledgements
This work was supported by State Grid Zhejiang Electric Power Corporation Technology Project (Grant No. 5211DS16001R).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Liu, Z., Wang, Z., Wei, B., Yuan, X. (2019). Analysis and Implementation of Multidimensional Data Visualization Methods in Large-Scale Power Internet of Things. In: Li, Q., Song, S., Li, R., Xu, Y., Xi, W., Gao, H. (eds) Broadband Communications, Networks, and Systems. Broadnets 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-030-36442-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-36442-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36441-0
Online ISBN: 978-3-030-36442-7
eBook Packages: Computer ScienceComputer Science (R0)