Enhancing Visual Analytics systems with guidance: A task-driven methodology

Abstract

Enhancing Visual Analytics (VA) systems with guidance, such as the automated provision of data-driven suggestions and answers to the user’s task, is becoming increasingly important and common. However, how to design such systems remains a challenging task. We present a methodology to aid and structure the design of guidance for enhancing VA solutions consisting of four steps: (S1) defining the target of analysis, (S2) identifying the user tasks, (S3) describing the guidance tasks, and (S4) placing guidance. In summary, our proposed methodology specifies a space of possible user tasks and maps them to the corresponding space of guidance tasks, using recent abstract task typologies for guidance and visualization. We exemplify this methodology through two case studies from the literature: Overview, a system for exploring and labeling document collections aimed at journalists, and DoRIAH, a system for historical imagery analysis. We show how our methodology enriches existing VA solutions with guidance and provides a structured way to design guidance in complex VA scenarios.