Mixed-initiative visual data analysis processes are characterized by the co-adaptation of users and systems over time. As the analysis progresses, both actors – users and systems – gather information, update their analysis behavior, and work on different tasks towards their respective goals. In this paper, we contribute a multigranular model of co-adaptive visual analysis that is centered around incremental learning goals derived from a hierarchical taxonomy of learning goals from pedagogy. Our model captures how both actors adapt their data-, task-, and user/system-models over time. We characterize interaction patterns in terms of the dynamics of learning and teaching that drive adaptation. To demonstrate our model’s applicability, we outline aspects of co-adaptation in related models of visual analytics and highlight co-adaptation in existing applications. We further postulate a set of expectations towards adaptation in mixed-initiative processes and identify open research questions and opportunities for future work in co-adaptation.

混合主动式可视化数据分析过程的特点是随着时间的推移用户和系统的共同适应。随着分析的进行，参与者——用户和系统——收集信息，更新他们的分析行为，并朝着各自的目标开展不同的任务。在本文中，我们提供了一个多粒度的自适应视觉分析模型，该模型以从教学法学习目标的分层分类法中得出的增量学习目标为中心。我们的模型捕捉了两个参与者如何随着时间的推移调整他们的数据、任务和用户/系统模型。我们根据推动适应的学习和教学动态来描述交互模式。为了证明我们模型的适用性，我们概述了视觉分析相关模型中协同适应的各个方面，并强调了现有应用程序中的协同适应。我们进一步假设了对混合倡议过程中的适应的一系列期望，并确定了未来共同适应工作的开放研究问题和机会。