Abstract Provenance Graphs: Anticipating and Exploiting Schema-Level Data Provenance

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 the new concept of Abstract Provenance Graphs (APGs). APGs are created via static analysis of a configured workflow W and input data schema, i.e., before W 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 \(\mathcal H_v\) 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. A more detailed version of this work is available as [14].