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The State-of-the-Art in Predictive Visual Analytics

Yafeng Lu, Rolando Garcia, Brett Hansen, Michael Gleicher, Ross Maciejewski
Computer Graphics Forum, Volume 36, Number 3 — Jun 2017
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    Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. In this state-of-the-art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.

    BibTex references

      author       = "Lu, Yafeng and Garcia, Rolando and Hansen, Brett and Gleicher, Michael and Maciejewski, Ross",
      title        = "The State-of-the-Art in Predictive Visual Analytics",
      journal      = "Computer Graphics Forum",
      number       = "3",
      volume       = "36",
      month        = "Jun",
      year         = "2017",
      note         = "EuroVis 2017 State of the Art Report (STAR)",
      url          = "http://graphics.cs.wisc.edu/Papers/2017/LGHGM17"

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