Frontiers of Computer Science

, Volume 11, Issue 2, pp 192–207 | Cite as

Recent progress and trends in predictive visual analytics

  • Junhua Lu
  • Wei Chen
  • Yuxin Ma
  • Junming Ke
  • Zongzhuang Li
  • Fan Zhang
  • Ross Maciejewski
Review Article

Abstract

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.

Keywords

predictive visual analytics visualization visual analytics data mining predictive analysis 

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Notes

Acknowledgements

This work was supported by National Basic Research Program of China (973 Program) (2015CB352503), Major Program of the National Natural Science Foundation of China (61232012), the National Natural Science Foundation of China (Grant Nos. 61303141, 61422211, u1536118, u1536119), Zhejiang Provincial Natural Science Foundation of China (LR13F020001), the Fundamental Research Funds for the Central Universities, the Innovation Joint Research Center for Cyber-Physical-Society System, and the United State’s National Science Foundation (1350573).

Supplementary material

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Supplementary material, approximately 273 KB.

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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Junhua Lu
    • 1
  • Wei Chen
    • 1
  • Yuxin Ma
    • 1
  • Junming Ke
    • 2
  • Zongzhuang Li
    • 1
  • Fan Zhang
    • 3
  • Ross Maciejewski
    • 4
  1. 1.State Key Lab of Computer Aided Design and Computer GraphicsZhejiang UniversityHangzhouChina
  2. 2.College of ScienceZhejiang University of TechnologyHangzhouChina
  3. 3.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina
  4. 4.School of Computing, Informatics and Decision Systems EngineeringArizona State UniversityTempe AZUSA

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