A taxonomy of visualization techniques using the data state reference model

By Ed H. Chi. IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings, 2000

646 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/caacee1bd480726b4d995926d395fce40a77f56a

How researchers see this paper

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

In Artificial Intelligence

1. Min Chen
2. Heike Leitte
3. Alan John Dix
4. Denis Parra
5. Enrico Bertini
6. Chen He
7. Michelle X. Zhou
8. David Gotz

In Computer Vision and Pattern Recognition

1. Min Chen
2. Heike Leitte
3. Haim Levkowitz
4. Maria Cristina Ferreira de Oliveira
5. Katrien Verbert
6. Denis Parra
7. Chen He
8. Geoffrey P. Ellis

Citations in the corpus

• From Visual Data Exploration to Visual Data Mining: A Survey (2003, background): “Chi [20] argues that, although taxonomies of visualization techniques based on the data domains are useful to end users, they do not help implementers to understand the design options and the potential applicability of such techniques.”
• prefuse: a toolkit for interactive information visualization (2005, methodology): “This model provides a sound base for characterizing a vast majority of infovis work (including the previous examples), providing a comprehensive taxonomy of visualization techniques [15].”
• Characterizing Users' Visual Analytic Activity for Insight Provenance (2008, background): “System-oriented taxonomies focus on describing visualization or data operations (e.g., [4, 5, 6, 28, 29]).”
• Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, methodology): “Several data type taxonomies have been described in literature (Chi, 2000; Keim, 2002; Adnan et al., 2008; Ellis and Dix, 2007).”
• An Information-theoretic Framework for Visualization (2010, methodology): “There are many variations, such as [6, 9, 18, 25].”
• Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization (2011, methodology): “The Data State Reference model [13] is largely based on the information visualization pipeline and classifies visualizations according to how they use the operators in the pipeline.”
• Rethinking Visualization: A High-Level Taxonomy (2004, background): “) [3, 5] or include generic tasks performed by users of the system (e.”
• A Taxonomy of Clutter Reduction for Information Visualisation (2007, background): “Chi’s taxonomy [13] takes a more detailed, processing-centric approach to classifying visualisation techniques going from ‘value’ to ‘view’ via analytical and visualisation abstractions.”