Co-Adaptive Visual Data Analysis And Guidance Processes

Mixed-initiative visual data analysis processes are characterized by the co-adaptation of users and sys-tems over time. As the analysis progresses, both actors - users and systems - gather information, up-date 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 high-light 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. (c) 2021 Elsevier Ltd. All rights reserved.