Dcv: a causality detection approach for large-scale dynamic collaboration environments

Abstract

Recent studies have indicated the significance of supporting real-time group editing in "Wiki" applications, whose collaboration environments have their dynamic and large-scale nature. Correct capture of causal relationships between operations from different users is crucial in order to preserve consistency of object copies. This challenge was resolved by employing vector logical clock. But since its size is equal to the number of cooperating sites, it has low efficiency when dealing with a collaborative environment involving a large number of participants. In this paper, we propose a direct causal vector (DCV) approach for solving causality detection issues in real-time group editors. DCV timestamp does not record the causality information that can be deduced from the transitivity of causal relation. As a result, it can automatically reduce its own size when people leave the collaboration session and always keep small. We prove that DCV approach is well fit for capturing causality in wiki like large-scale dynamic collaboration environments.