Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.
, it can be seen, that most of the discussed approaches cover how explicit domain knowledge can be exploited to enhance visual representation and data analysis; some approaches provide methods to generate explicit knowledge. Additionally, most of the currently implemented knowledge-assisted VA environments are focused on the integration of specific domain knowledge, which could only be used for precisely defined analysis tasks. In general, explicit knowledge is now a first-class artifact in the VA process but its form and structure are left unspecified. None of the presented approaches provides a structural framework for describing and storing explicit knowledge in VA environments. Thus, a structural framework is needed and combined with the theoretical process model by Federico, @cite_4 it would provide valuable generative guidelines for the development of novel knowledge-assisted VA environments.
@cite_4
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Visual Analytics (VA) aims to combine the strengths of humans and computers for effective data analysis. In this endeavor, humans’ tacit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the analytic process. While VA environments are starting to include features to formalize, store, and utilize such knowledge, the mechanisms and degree in which these environments integrate explicit knowledge varies widely. Additionally, this important class of VA environments has never been elaborated on by existing work on VA theory. This paper proposes a conceptual model of Knowledge-assisted VA conceptually grounded on the visualization model by van Wijk. We apply the model to describe various examples of knowledge-assisted VA from the literature and elaborate on three of them in finer detail. Moreover, we illustrate the utilization of the model to compare different design alternatives and to evaluate existing approaches with respect to their use of knowledge. Finally, the model can inspire designers to generate novel VA environments using explicit knowledge effectively.