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Journal of Visualization

, Volume 22, Issue 6, pp 1241–1255 | Cite as

RankBrushers: interactive analysis of temporal ranking ensembles

  • Dongming Han
  • Jiacheng Pan
  • Fangzhou Guo
  • Xiaonan Luo
  • Yingcai Wu
  • Wenting Zheng
  • Wei ChenEmail author
Regular Paper
  • 71 Downloads

Abstract

Temporal ranking ensembles indicate time-evolving multivariate rankings. Such data can be commonly found in our daily life, for example, different rankings of universities (QS, ARWU, THE, and USNews) over year and those of NBA players over season. Effective analysis and tracking of rankings allow users to gain insights into the overall ranking change over time and seek the explanation for the change. This paper introduces a novel visual analytics approach for characterizing and visualizing the uncertainty, dynamics, and differences of ranking ensemble data. A novel visual design is proposed to characterize the evolution pattern, distribution, and uncertainty of a large number of temporal ranking ensembles. The evolutionary ranking ensembles are progressively explored, tracked, and compared by means of an intuitive visualization system. Two case studies and a task-driven user study conducted on real datasets demonstrate the effectiveness and feasibility of the implemented system.

Graphic abstract

Keywords

Visualization Temporal ranking ensembles Uncertainty 

Notes

Acknowledgements

This research supported by National Key Research and Development Program (2018YFB0904503) and National Natural Science Foundation of China (U1866602, 61772456, 61761136020).

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Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  • Dongming Han
    • 1
  • Jiacheng Pan
    • 1
  • Fangzhou Guo
    • 1
  • Xiaonan Luo
    • 2
  • Yingcai Wu
    • 1
  • Wenting Zheng
    • 1
  • Wei Chen
    • 1
    Email author
  1. 1.State Key Lab of CAD and CGZhejiang UniversityHangzhouChina
  2. 2.Guilin University of Electronic TechnologyGuilinChina

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