Publikation: Co-adaptive visual data analysis and guidance processes
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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.
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SPERRLE, Fabian, Astrik Veronika JEITLER, Jürgen BERNARD, Daniel A. KEIM, Mennatallah EL-ASSADY, 2021. Co-adaptive visual data analysis and guidance processes. In: Computers & Graphics. Elsevier. 2021, 100, pp. 93-105. ISSN 0097-8493. eISSN 1873-7684. Available under: doi: 10.1016/j.cag.2021.06.016BibTex
@article{Sperrle2021Coada-54358, year={2021}, doi={10.1016/j.cag.2021.06.016}, title={Co-adaptive visual data analysis and guidance processes}, volume={100}, issn={0097-8493}, journal={Computers & Graphics}, pages={93--105}, author={Sperrle, Fabian and Jeitler, Astrik Veronika and Bernard, Jürgen and Keim, Daniel A. and El-Assady, Mennatallah} }
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