IEEE Trans Vis Comput Graph. 2022 Oct;28(10):3563-3584. doi: 10.1109/TVCG.2021.3064037. Epub 2022 Sep 1.
In the field of information visualization, the concept of "tasks" is an essential component of theories and methodologies for how a visualization researcher or a practitioner understands what tasks a user needs to perform and how to approach the creation of a new design. In this article, we focus on the collection of tasks for tree visualizations, a common visual encoding in many domains ranging from biology to computer science to geography. In spite of their commonality, no prior efforts exist to collect and abstractly define tree visualization tasks. We present a literature review of tree visualization articles and generate a curated dataset of over 200 tasks. To enable effective task abstraction for trees, we also contribute a novel extension of the Multi-Level Task Typology to include more specificity to support tree-specific tasks as well as a systematic procedure to conduct task abstractions for tree visualizations. All tasks in the dataset were abstracted with the novel typology extension and analyzed to gain a better understanding of the state of tree visualizations. These abstracted tasks can benefit visualization researchers and practitioners as they design evaluation studies or compare their analytical tasks with ones previously studied in the literature to make informed decisions about their design. We also reflect on our novel methodology and advocate more broadly for the creation of task-based knowledge repositories for different types of visualizations. The Supplemental Material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2021.3064037, will be maintained on OSF: https://osf.io/u5ehs/.
在信息可视化领域,“任务”的概念是理解用户需要执行什么任务以及如何创建新设计的理论和方法的重要组成部分。在本文中,我们专注于树可视化任务的收集,树可视化是从生物学到计算机科学到地理学等许多领域的常见视觉编码。尽管它们具有共性,但之前没有收集和抽象定义树可视化任务的工作。我们对树可视化文章进行了文献综述,并生成了一个包含 200 多个任务的精心策划的数据集。为了有效地进行树任务抽象,我们还对多级别任务分类法进行了新颖的扩展,以包含更多的特异性,以支持树特定任务,并提供了一种系统的程序来进行树可视化的任务抽象。数据集中的所有任务都使用新颖的分类法扩展进行了抽象,并进行了分析,以更好地了解树可视化的现状。这些抽象任务可以使可视化研究人员和从业者受益,因为他们设计评估研究或比较他们的分析任务与文献中以前研究过的任务,以便就他们的设计做出明智的决策。我们还反思了我们的新方法,并提倡为不同类型的可视化创建基于任务的知识库。补充材料可在计算机协会数字图书馆 http://doi.ieeecomputersociety.org/10.1109/TVCG.2021.3064037 上找到,并将在 OSF 上维护:https://osf.io/u5ehs/。
IEEE Trans Vis Comput Graph. 2022-10
IEEE Trans Vis Comput Graph. 2023-1
JMIR Med Inform. 2018-7-9
IEEE Trans Vis Comput Graph. 2025-1
Nucleic Acids Res. 2019-7-2
IEEE Trans Vis Comput Graph. 2022-12
Mol Biol Evol. 2016-8
IEEE Trans Vis Comput Graph. 2022-12
IEEE Trans Vis Comput Graph. 2022-12
IEEE Trans Vis Comput Graph. 2024-1
Comput Graph Forum. 2023-9