IEEE Trans Vis Comput Graph. 2021 Aug;27(8):3410-3424. doi: 10.1109/TVCG.2020.2977634. Epub 2021 Jun 30.
Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This article presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool-MediSyn-for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study's implications, lessons learned, and future research opportunities.
随着允许用户在视觉探索过程中发表评论的趋势的发展，理解洞察的质量变得越来越重要，但是定性洞察的方法却很少。本文通过研究交互作用的可能性，提出了一个案例研究，以调查通过执行的交互作用来描述洞察质量的可能性。为此，我们设计了可视化工具 MediSyn 的交互作用，以生成洞察。MediSyn 支持五种类型的交互作用：选择、连接、阐述、探索和共享。我们通过允许 14 名参与者自由探索数据并生成洞察，对 MediSyn 进行了评估。然后，我们从参与者的交互日志中提取了七种交互模式，并将这些模式与洞察质量的四个方面相关联。结果表明，通过交互作用可以定性洞察。其他发现包括：探索行为可以产生意想不到的洞察；向下钻取模式往往会增加洞察的领域值。定性分析表明，使用领域知识来指导探索可以积极影响派生洞察的领域值。我们讨论了研究的意义、经验教训和未来的研究机会。
