A data model for analyzing user collaborations in workflow-driven e-science

Scientific discoveries are often the result of methodical execution of many interrelated scientific workflows, where workflows and datasets published by one set of users can be used by other users to perform subsequent analyses, leading to implicit or explicit collaboration. In this paper, we describe a data model for "collaborative provenance" that extends common workflow provenance models by introducing attributes for characterizing the nature of user collaborations as well as their strength (or weight). In addition, through the implementation of a real-world bioinformatics use case scenario and an associated collaborative provenance database, we demonstrate and evaluate the effectiveness of our model in understanding and analyzing user collaboration in scientific discoveries driven by scientific workflows.