Information Visualization in Co-located Collaborative Environments
Collaboration around visualizations can have a number of benefits compared to individual work. In many disciplines, collaboration allows for a multi-disciplinary groups with an increased skill set. Different team members offer different perspectives and expertise that together can improve the quality of solutions and decisions. Also, the analyzed information space may often simply be too complex for an individual to interpret in its entirety. With large data sets, even the task load of exploring the data could be shared among several individuals on a team. The benefits that collaboration offers to this process have motivated my shift from single-user information visualization tools toward research on the design of collaborative information visualization tools.
Finding ways to augment this type of collaborative data analysis with the power of digital information visualizations may lead to more effective decision making. Most techniques so far have been designed to support this effective decision making for a single analyst by providing new techniques to conquer problems such as those of displaying increasingly large and multi-dimensional data sets, finding appropriate visual support for relational information like hierarchies, clusters, temporal trends, outliers, or providing appropriate interaction techniques to explore complex datasets. Often neglected in this research is the notion that data analysis is also commonly a social experience. With large data sets, the task load of exploring the data could be shared among several individuals on a team. Datasets on which decision and discoveries are based may also be susceptible to a variety of interpretations, in which case experts may discuss and negotiate their interpretations of the data. Motivated by these benefits of collaborative data analysis, my dissertation investigates the challenges of supporting data analysis for co-located, synchronous work environments.
Here is a video of how co-located collaborative data analysis should not take place.
|(:media-player (http://pages.cpsc.ucalgary.ca/~pneumann/projects/collabvis/)SingleScreenInfovis2(.flv) width=320 height=260 text="How not to do collaborative data analysis" image="" align='middle' -link:)|
- Only one person can interact with the visualization at the same time
- Not everyone can see the visualization properly
- People can't see each other properly and have face-to-face discussions around the data
What am I doing?:
- My research tries to provide better collaborative work environments for co-located data analysis in particular:
- using large screen, mulit-touch technology
- by studying how people naturally collaborate around visualizations and by supporting that process through the right software (look at my first implementation below)
Video of Collaborative Tree Comparison System
|System in Use|
|(:flv-player (http://pages.cpsc.ucalgary.ca/~pneumann/projects/collabvis/)CollabTreeComparison(.flv) width=320 height=260 text="Two people using our system for collaborative tree comparison" image="" align='middle' -link:)|
|(:flv-player (http://pages.cpsc.ucalgary.ca/~pneumann/projects/collabvis/)Neumann_2007_ITC(.flv) width=320 height=240 text="A simple video explaining the features of our collaborative tree comparison system" image="" align='middle' -link:)|
Annotations on Tabletop Displays
This video isn't directly about collaborative information visualization but the technique still has implications for it. The underlying problem is that it is hard to use a finger on tabletops for fine-grained interaction - here for writing small annotations. We showed this technique for annotation of photo collections but it can just as well be used to annotate graphs, charts, or other visualization (and if you watched the videos above you see that I reused this technique for such a scenario).