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P. Federico, M. Wagner, A. Rind, A. Amor-Amoros, S. Miksch, W. Aigner:
"The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics";
Talk: IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017), Phoenix, AZ; 2017-10-01 - 2017-10-06; in: "Proceedings of the IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017)", (2017), 1 - 12.

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.

http://publik.tuwien.ac.at/files/publik_261674.pdf