1997IEEE VisualizationRequires access

Collaborative visualization

Jason Wood, Helen Wright, Ken Brodlie

Open publisher page 114 citations

Abstract

Current visualization systems are designed around a single user model, making it awkward for large research teams to collectively analyse large data sets. The paper shows how the popular data flow approach to visualization can be extended to allow multiple users to collaborate-each running their own visualization pipeline but with the opportunity to connect in data generated by a colleague, Thus collaborative visualizations are 'programmed' in exactly the same 'plug-and-play' style as is now customary for single-user mode. The paper describes a system architecture that can act as a basis for the collaborative extension of any data flow visualization system, and the ideas are demonstrated through a particular implementation in terms of IRIS Explorer.

About this research paper

What this paper is about

Current visualization systems are designed around a single user model, making it awkward for large research teams to collectively analyse large data sets. The paper shows how the popular data flow approach to visualization can be extended to allow multiple users to collaborate-each running their own visualization pipeline but with the opportunity to connect in data generated by a colleague, Thus collaborative visualizations are 'programmed' in exactly the same 'plug-and-play' style as is now customary for single-user mode. The paper describes a system architecture that can act as a basis for the collaborative extension of any data flow visualization system, and the ideas are demonstrated through a particular implementation in terms of IRIS Explorer.

Why it matters

OpenAlex reports 114 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

Current visualization systems are designed around a single user model, making it awkward for large research teams to collectively analyse large data sets. The paper shows how the popular data flow approach to visualization can be extended to allow multiple users to collaborate-each running their own visualization pipeline but with the opportunity to connect in data generated by a colleague, Thus collaborative visualizations are 'programmed' in exactly the same 'plug-and-play' style as is now customary for single-user mode. The paper describes a system architecture that can act as a basis for the collaborative extension of any data flow visualization system, and the ideas are demonstrated through a particular implementation in terms of IRIS Explorer.

Key concepts: Visualization, Computer science, Data visualization, Pipeline (software), Human–computer interaction, Information visualization, Architecture, World Wide Web

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