Görg Carsten, Stasko John
IEEE Trans Vis Comput Graph. 2011 May;17(5):570-83. doi: 10.1109/TVCG.2010.84. Epub 2010 Jun 7.
Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations on metrics and techniques for evaluating visual analytics systems for investigative analysis.
尽管越来越多的系统为调查分析提供可视化分析支持，但针对此类系统潜在益处的实证研究却很少，尤其是对照性的比较评估。然而，确定此类系统如何促进洞察力和意义建构对于它们的持续发展和研究至关重要。此外，识别人们如何使用此类系统以及他们为何受益（或未受益）的研究有助于为此领域新系统的设计提供参考。我们对在一项小型调查意义建构练习中使用的可视化分析系统Jigsaw进行了评估，并将其使用情况与其他三种更传统的分析方法进行了比较。16名参与者在四种条件之一的情况下执行了模拟情报分析任务。实验结果表明，Jigsaw帮助参与者分析数据并识别出一个潜在威胁。我们描述了研究参与者使用的不同分析策略，以及计算支持（或缺乏计算支持）如何影响这些策略。然后，我们阐述了研究中确定的意义建构过程的几个特征，并据此为调查分析工具提供设计建议。最后，我们给出了评估用于调查分析的可视化分析系统的指标和技术建议。
