Domova Veronika, Vrotsou Katerina
IEEE Trans Vis Comput Graph. 2023 Aug;29(8):3550-3568. doi: 10.1109/TVCG.2022.3163765. Epub 2023 Jun 29.
The continuous growth in availability and access to data presents a major challenge to the human analyst. As the manual analysis of large and complex datasets is nowadays practically impossible, the need for assisting tools that can automate the analysis process while keeping the human analyst in the loop is imperative. A large and growing body of literature recognizes the crucial role of automation in Visual Analytics and suggests that automation is among the most important constituents for effective Visual Analytics systems. Today, however, there is no appropriate taxonomy nor terminology for assessing the extent of automation in a Visual Analytics system. In this article, we aim to address this gap by introducing a model of levels of automation tailored for the Visual Analytics domain. The consistent terminology of the proposed taxonomy could provide a ground for users/readers/reviewers to describe and compare automation in Visual Analytics systems. Our taxonomy is grounded on a combination of several existing and well-established taxonomies of levels of automation in the human-machine interaction domain and relevant models within the visual analytics field. To exemplify the proposed taxonomy, we selected a set of existing systems from the event-sequence analytics domain and mapped the automation of their visual analytics process stages against the automation levels in our taxonomy.
数据可用性和获取途径的持续增长给人类分析师带来了重大挑战。如今，对大型复杂数据集进行人工分析几乎是不可能的，因此迫切需要辅助工具来自动化分析过程，同时让人类分析师参与其中。大量且不断增长的文献认识到自动化在视觉分析中的关键作用，并表明自动化是有效视觉分析系统的最重要组成部分之一。然而，目前尚无合适的分类法或术语来评估视觉分析系统中的自动化程度。在本文中，我们旨在通过引入一个为视觉分析领域量身定制的自动化级别模型来填补这一空白。所提出分类法的一致术语可为用户/读者/评审人员描述和比较视觉分析系统中的自动化提供依据。我们的分类法基于人机交互领域中现有的几个成熟的自动化级别分类法以及视觉分析领域内的相关模型的组合。为了举例说明所提出的分类法，我们从事件序列分析领域中选择了一组现有系统，并将其视觉分析过程阶段的自动化与我们分类法中的自动化级别进行映射。
