The value of visualization
By Jack van Wijk. VIS 05. IEEE Visualization, 2005., 2005
522 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/3b4700859c370c8a065f7019987b097ba06eec25
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
- Florian Mansmann
- Tobias Isenberg
- Jörn Schneidewind
- Petra Isenberg
- Wil van der Aalst
- Torsten Möller
- Michael Sedlmair
- Jing Chen
Citations in the corpus
- Visual Analytics: Definition, Process, and Challenges (2008, unknown): “How to assess the value of visualization is a topic of lively debate [42,33].”
- Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together? (2013, unknown): “[29] provided a formal description of the visual analytics process, following some notions of Van Wijk [32], which demonstrates a core research of HCI relevant to solve problems involved with complex data: Let the input data sets be , .”
- Opportunities and Challenges for Data Physicalization (2015, unknown): “Given the focus on data representation, many of the evaluation issues are shared by the Visualization community [37, 50, 74].”
- A Systematic Review on the Practice of Evaluating Visualization (2013, methodology): “Van Wijk [69, 70] employed an economic model to assess the “value of visualization” based on effectiveness and efficiency.”
- Process Mining (2016, background): “The perception capabilities of the human cognitive system can be exploited by using the right visualizations [178].”
- Visual Analytics: Scope and Challenges (2008, background): “van Wijk, “visualization is not ’good’ by definition, developers of new methods have to make clear why the information sought cannot be extracted automatically” [6].”
- Challenges in Visual Data Analysis (2006, background): “’good’ by definition, developers of new methods have to make clear why the information sought cannot be extracted automatically” [13].”
- Provenance and scientific workflows: challenges and opportunities (2008, unknown): “[109] J.”
- Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, unknown): “bution (DOI j ∈ [0, 1] for each data item j, compare to the DOI information in generalized fisheye views [79]).”