Collaborative visualization: Definition, challenges, and research agenda
By Petra Isenberg, Niklas Elmqvist, Jean C. Scholtz, D. Cernea, Kwan-Liu Ma, H. Hagen. Information Visualization, 2011
328 citations recorded by the source corpus.
The conflux of two growing areas of technology – collaboration and visualization – into a new research direction, collaborative visualization, provides new research challenges. Technology now allows us to easily connect and collaborate with one another – in settings as diverse as over networked computers, across mobile devices, or using shared displays such as interactive walls and tabletop surfaces. Digital information is now regularly accessed by multiple people in order to share information, to view it together, to analyze it, or to form decisions. Visualizations are used to deal more effectively with large amounts of information while interactive visualizations allow users to explore the underlying data. While researchers face many challenges in collaboration and in visualization, the emergence of collaborative visualization poses additional challenges, but it is also an exciting opportunity to reach new audiences and applications for visualization tools and techniques. The purpose of this article is (1) to provide a definition, clear scope, and overview of the evolving field of collaborative visualization, (2) to help pinpoint the unique focus of collaborative visualization with its specific aspects, challenges, and requirements within the intersection of general computer-supported cooperative work and visualization research, and (3) to draw attention to important future research questions to be addressed by the community. We conclude by discussing a research agenda for future work on collaborative visualization and urge for a new generation of visualization tools that are designed with collaboration in mind from their very inception.
Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/f04ff25b1c3cd51e507dfe527a5ef0c936a2ab67
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
- Ben Shneiderman
- Catherine Plaisant
- Michael Sedlmair
- Neil T. Spring
- Kenyon Crowley
- Fan Du
- Petra Isenberg
- Tobias Isenberg
In Sociology and Political Science
- Michael Sedlmair
- Ben Shneiderman
- Petra Isenberg
- Catherine Plaisant
- Tobias Isenberg
- Torsten Möller
- Jing Chen
- Tamara Munzner
In Human-Computer Interaction
- Catherine Plaisant
- Michael Sedlmair
- Ben Shneiderman
- Petra Isenberg
- Torsten Möller
- Tamara Munzner
- Matthew Brehmer
- Siddhartha Chaudhuri
Citations in the corpus
- EventAction (2019, background): “[31], one unique challenge in supporting collaboration in EventAction is that the collaborators play asymmetric roles: (1) advisors are usually familiar with the system and thus can fully understand the visualizations and confidently use the controls while advisees are typically novice users who pre”
- A Systematic Review on the Practice of Evaluating Visualization (2013, background): “While visualization for collaborative analysis has just recently gained more attention [25], it has not been a very strong topic in the conference—perhaps explaining the lack of evaluations.”