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.
Key Words: Collaborative e-Science, user collaborations,
scientific workflow systems, provenance, workflow runs, data
publication, querying.