Saraiya Purvi, North Chris, Lam Vy, Duca Karen A
Department of Computer Science, Virginia Tech, Blacksburg 24061-0106, USA.
IEEE Trans Vis Comput Graph. 2006 Nov-Dec;12(6):1511-22. doi: 10.1109/TVCG.2006.85.
Visualization tools are typically evaluated in controlled studies that observe the short-term usage of these tools by participants on preselected data sets and benchmark tasks. Though such studies provide useful suggestions, they miss the long-term usage of the tools. A longitudinal study of a bioinformatics data set analysis is reported here. The main focus of this work is to capture the entire analysis process that an analyst goes through from a raw data set to the insights sought from the data. The study provides interesting observations about the use of visual representations and interaction mechanisms provided by the tools, and also about the process of insight generation in general. This deepens our understanding of visual analytics, guides visualization developers in creating more effective visualization tools in terms of user requirements, and guides evaluators in designing future studies that are more representative of insights sought by users from their data sets.
可视化工具通常在对照研究中进行评估，这些研究观察参与者在预先选定的数据集和基准任务上对这些工具的短期使用情况。尽管此类研究提供了有用的建议，但它们忽略了工具的长期使用情况。本文报告了一项对生物信息学数据集分析的纵向研究。这项工作的主要重点是捕捉分析师从原始数据集到从数据中寻求见解的整个分析过程。该研究提供了关于工具所提供的视觉表示和交互机制的使用情况的有趣观察结果，以及关于一般见解生成过程的观察结果。这加深了我们对视觉分析的理解，指导可视化开发人员根据用户需求创建更有效的可视化工具，并指导评估人员设计更能代表用户从其数据集中寻求的见解的未来研究。
