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

Abstract

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.

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

“…Lu et al [LGH∗17] conducted a survey focused on predictive visual analytics (PVA) and introduced an additional interaction type ‘Shepherd’ that we also used as a subtype in our interaction taxonomy.…”

“…Yi et al [YaKSJ07] provided the taxonomy of interaction by defining seven interaction categories. Lu et al [LGH∗17], in their predictive visual analytics survey, not only used this interaction taxonomy but also added a Shepherd technique that helps users to tune the modeling process. We also coded our surveyed papers (Table 3) based on the interaction techniques taxonomy used by Lu et al [LGH∗17].…”

“…Lu et al [LGH∗17], in their predictive visual analytics survey, not only used this interaction taxonomy but also added a Shepherd technique that helps users to tune the modeling process. We also coded our surveyed papers (Table 3) based on the interaction techniques taxonomy used by Lu et al [LGH∗17]. Below we discuss these interaction techniques with examples.…”

“…The Shepherd [LGH∗17] interaction technique enables users to tune the modelling process either directly or indirectly. Direct shepherding allows users to choose model parameters settings such as updating number of clusters in k‐means.…”

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

“…Since the term Visual Analytics (VA) was coined in 2004 [WT04, TC05], there have been hundreds of software systems reported in the literature for supporting various VA workflows (e.g., [ERT∗17, LGH∗17,CLY17]). As articulated by Thomas and Kielman [TK09] and Robertson et.…”