Li, Jianping KelvinXu, ShenyuYe, Yecong (Chris)Ma, Kwan-LiuViola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana2020-05-242020-05-2420201467-8659https://doi.org/10.1111/cgf.13997https://diglib.eg.org:443/handle/10.1111/cgf13997Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.Attribution 4.0 International License10.1111/cgf.13997497-509