Knowledge Generation Model for Visual Analytics

By D. Sacha, A. Stoffel, Florian Stoffel, Bum Chul Kwon, Geoffrey P. Ellis, Daniel A. Keim. IEEE Transactions on Visualization and Computer Graphics, 2014

421 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f31c3bc7c2d63b28ee53b807ee9cffcbc6208901

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Geoffrey P. Ellis
2. Daniel A. Keim
3. Bum Chul Kwon
4. Hansi Senaratne
5. Sukwon Lee
6. Sung-Hee Kim
7. Niklas Elmqvist
8. Jean-Daniel Fekete

Citations in the corpus

• The Role of Uncertainty, Awareness, and Trust in Visual Analytics (2016, methodology): “In the following, we walk through the human concepts of the knowledge generation model [64] and describe them with respect to trust building and highlight influences that are caused by uncertainties.”
• VLAT: Development of a Visualization Literacy Assessment Test (2017, background): “Furthermore, we need to understand perceptual skills for pattern detection and cognitive skills to process the series of tasks efficiently for analyzing data, gaining insights, and generating knowledge [43].”