Friday, November 14, 2014

Collaborative visualization: Definition, challenges, and research agenda

By: Petra Isenberg, Niklas Elmqvist, Jean Scholtz, Daniel Cernea, Kwan-Liu Ma, Hans Hagen

According to the authors of this research paper, “collaboration has been named one of the grand challenges for visualization and visual analytics.” Traditionally, visualization and visual analytic tools were designed for a single person on a desktop computer. However, today’s world calls for increased visualization tools that encompass collaboration and communication. Experts and non-experts can take advantage of collaborative visualization scenarios to learn from one another’s analysis processes and viewpoints. The authors define collaborative visualization as “the shared use of computer-supported, [interactive], visual representations of data by more than one person with the common goal of contribution to joint information processing activities.” The term social data analysis has also been created to describe the different social interactions, which is central to collaborative visualization.

There are three main levels of engagement where digital systems support collaborative visualizations: viewing, interacting/exploring, and sharing/creating. Software systems like PowerPoint and videoconferencing allow people to learn, discuss, interpret and form decisions on a certain set of information. People that use and share interactive visualization software can communicate through chat, comments, email, or video/audio links. Utilizing these features allows discussions of alternative interpretations, and multiple viewpoints to emerge. Programs such as Many Eyes, allow users to upload and create new datasets for the community to explore.  The authors present this argument that the purpose of having an online collaboratory (data warehouse) “is to focus the collective effort of the group in order to produce significant and useful methods.” However, it is important for the users of the program to understand the overall data, the user space and the application space.

Computer-supported collaborative visualization software helps decision makers: distill knowledge through mining large multi-dimensional datasets, run models and simulation to explore the consequences of particular actions, communicate results, scenarios, and opinions to other stakeholders, and discuss debate, and develop support for specific courses of action. In addition, collaborative technology supports the social interaction of large audiences, which allows for a range of backgrounds, connections and goals. This provides the group with an environment where individuals can generate ideas and analysis alone or together.

This article gave a broad overview of collaborative visualization and the areas where future research should be addressed. However, the authors did not integrate the challenges of collaborative visualization throughout the piece, instead they arranged it in future research. As collaborative visualization becomes utilized as an everyday tool, it will be important for people to learn these programs at school or at work. Knowing how these analytic tools work will be key to group interactions and their analyses.


Isenberg, P., Elmqvist, N., Scholtz, J., Cernea, D., Ma, K.-L., & Hagen, H. (2011). Collaborative visualization: Definition, challenges, and research agenda. Information Visualization, 10(4), 310–326. doi:10.1177/1473871611412817


  1. Joy,

    Which of the three levels of collaborative visualization have you found yourself spending the most time doing?

  2. Did this article cite any previous studies that even warrant future research into collaborative visualization. For instance, does it generate more ideas in brainstorming stages?

  3. Harrison,
    Out of the three levels of collaborative visualization, I have used viewing and interacting/exploring the most.

    The authors of the study presented a large section on future research, however, they did not go into past studies on brainstorming stages. The study also lacked a coherent focus on the different types of future research.