Distributed Collaborative Analytics

Complexity, variety and extensiveness of required information for analyzing real world problems and situations make it hard for individuals to effectively address them. Analysts should be able to collaboratively work on the huge amount of available information and share their findings and understandings to effectively and efficiently make sense of the situation under investigation.

The overall research question this project addresses is: How can a collaborative analytics framework support efficient and effective distributed (in time and space) collaboration among analysts? And the more specific question we focus on is: How can a collaborative analytics framework support efficient and effective reuse of the reasoning artifacts (e.g. argument, scenario, causal map, etc.)?

We have designed a collaborative visual analytics system, AnalyticStream, to investigate this question. Based on our preliminary research, our design aims for two major goals: first, to facilitate and encourage sharing and reuse of analysis results in the form of reusable reasoning artefacts, and second, to facilitate collaborative sensemaking through story-like knowledge structures.

Through deepening our understanding of the individual and collaborative sensemaking processes that analysts go through, we have proposed design considerations for facilitating these processes, fostering collaboration, and improving collaboration efficiency. Ultimately, these design considerations have informed the design of AnalyticStream and the evaluation of the system will contribute to our understanding of the guidelines while enabling us refine them.

Paper: 

Contact:

nobarany <at> gmail <dot> com