Supporting Awareness through Collaborative Brushing and Linking of Tabular Data

Maintaining an awareness of collaborators´ actions is critical during collaborative work, including during collaborative visualization activities. Particularly when collaborators are located at a distance, it is important to know what everyone is working on in order to avoid duplication of effort, share relevant results in a timely manner and build upon each other´s results. Can a person´s brushing actions provide an indication of their queries and interests in a data set? Can these actions be revealed to a collaborator without substantially disrupting their own independent work? We designed a study to answer these questions in the context of distributed collaborative visualization of tabular data. Participants in our study worked independently to answer questions about a tabular data set, while simultaneously viewing brushing actions of a fictitious collaborator, shown directly within a shared workspace. We compared three methods of presenting the collaborator´s actions: brushing & linking (i.e. highlighting exactly what the collaborator would see), selection (i.e. showing only a selected item), and persistent selection (i.e. showing only selected items but having them persist for some time). Our results demonstrated that persistent selection enabled some awareness of the collaborator´s activities while causing minimal interference with independent work. Other techniques were less effective at providing awareness, and brushing & linking caused substantial interference. These findings suggest promise for the idea of exploiting natural brushing actions to provide awareness in collaborative work.