Ceneda Davide, Andrienko Natalia, Andrienko Gennady, Gschwandtner Theresia, Miksch Silvia, Piccolotto Nikolaus, Schreck Tobias, Streit Marc, Suschnigg Josef, Tominski Christian
Vienna University of Technology Austria.
Fraunhofer Institute IAIS Germany.
Comput Graph Forum. 2020 Sep;39(6):269-288. doi: 10.1111/cgf.14017. Epub 2020 May 16.
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.
引导是视觉分析领域中一个新兴的话题。引导可以帮助用户更高效地实现其分析目标，并有助于使分析取得成功。然而，目前尚不清楚引导方法应如何设计，以及为了提供有效的支持应考虑哪些具体因素。在本文中，我们从引导设计者的角度来探讨这个问题。我们提出了一个框架，该框架包括需求以及设计者在为视觉分析设计引导时应经历的一组特定阶段。我们将这个过程与一组质量标准相关联，我们旨在通过我们的框架来支持这些标准，这些标准对于获得合适且有效的引导解决方案是必要的。为了证明我们方法的实际可用性，我们将我们的框架应用于三种分析场景中的引导设计以及一次设计演练环节。此外，我们列出了新出现的挑战，并报告了该框架如何用于设计能够缓解这些问题的引导解决方案。
