Just-in-time interactive analytics: Guiding visual exploration of data


The increasing complexity and volume of data mandate tighter integration between analytics and visualization. During this paper, I propose a pattern of integrating of computational and visual analytics techniques, called simply-in-time (JIT) interactive analytics. JIT analytics is performed in real-time on data that users are interacting with to guide visual-analytic exploration. Basic to JIT analytics is enriching visualizations with annotations that describe semantics of visual features, thereby suggesting to users attainable insights to look at additional. To accomplish this, JIT analytics desires to 1) identify insights depicted as visual patterns such as clusters, outliers, and trends in visualizations and 2) determine the semantics of such options by considering not solely attributes that are being visualized but also different attributes in information. During this paper, I describe the JIT interactive analytics pattern, along with a generic implementation for any kind of visualization and data, and offer a specific implementation for purpose-primarily based visualization of multivariate knowledge. I argue that the pattern provides a helpful user experience by elevating the cognitive level of interaction with knowledge from pure perception of visual representations to understanding higher level semantics of knowledge. As such, this supports users in building faster qualitative mental models and accelerating discovery. Furthermore, facilitating insight opens new analysis opportunities such as visual-analytic action recommendations, improved collaboration, and accessibility.

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