Abstract. Provenance graphs capture flow and dependency information recorded during scientific workflow runs, which can be used subsequently to interpret, validate, and debug workflow results. In this paper, we propose a new concept, called abstract provenance graph (APG). APGs are created via static analysis of a configured workflow W and input data schema τ, i.e., before the workflow is actually executed. They summarize all possible provenance graphs the workflow W can create with input data of type τ, that is, for each input v ∈ τ there exists a graph homomorphism H v between the concrete and abstract provenance graph. APGs are helpful during workflow construction since (1) they make certain workflow design-bugs (e.g., selecting none or wrong input data for the actors) easy to spot; and (2) show the evolution of the overall data organization of a workflow. Moreover, after workflows have been run, APGs can be used to validate concrete provenance graphs.
Likewise, "abstract provenance graph" REF are derived by static analysis of workflows.
Abstract Provenance Graphs: Anticipating and Exploiting Schema-Level Data Provenance
IPAW
Computer Science