Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis

Jianping Kelvin Li, Shenyu Xu, Yecong Ye, Kwan Liu Ma

Research output: Contribution to journalArticlepeer-review


Analyzing 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.

Original languageEnglish (US)
Pages (from-to)497-509
Number of pages13
JournalComputer Graphics Forum
Issue number3
StatePublished - Jun 1 2020

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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