Abstract
Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk‐through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.
Highlights
Visual analytics (VA) approaches can be effective tools for making sense of large datasets and perform complex tasks
We present a design framework and a set of qualitative requirements to guide the design of effective guidance in VA
To previous research that focused on describing the characteristics of the process of guiding [CGM19], our goal has been to describe the process of designing guidance and to present it as a sequence of steps applicable to a wide variety of analysis scenarios
Summary
Visual analytics (VA) approaches can be effective tools for making sense of large datasets and perform complex tasks Their strengths come from a tight integration of automated analysis methods and visual interactive interfaces [TC05]. Despite the development of guidelines and the adoption of well-established design patterns [DFAB04, DAREA*18], using interactive interfaces may present many challenges to analysts. Given these premises, the research community started to develop approaches and techniques to support data analysts during the analysis process. The research community started to develop approaches and techniques to support data analysts during the analysis process Interest in guidance is quite recent and research has just started
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