Abstract
Provenance traces history within workflows and enables researchers to validate and compare their results. Currently, modelling provenance in ProvONE is an arduous task and lacks an automated approach. This paper introduces a novel algorithm, called Prov2ONE that automatically generates the ProvONE prospective provenance for scientific workflows defined in BPEL4WS. The same prospective ProvONE graph is updated with the relevant retrospective provenance, preventing provenance to be captured in various non-standard provenance models and thus enabling research communities to share, compare and analyze workflows and its associated provenance. Finally, using the Prov2ONE algorithm, a ProvONE provenance graph for the nanoscopy workflow is generated.
Cite
CITATION STYLE
Prabhune, A., Zweig, A., Stotzka, R., Gertz, M., & Hesser, J. (2016). Prov2ONE: An algorithm for automatically constructing ProvONE provenance graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9672, pp. 204–208). Springer Verlag. https://doi.org/10.1007/978-3-319-40593-3_22
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