P-PIF: a ProvONE provenance interoperability framework for analyzing heterogeneous workflow specifications and provenance traces

摘要

AbstractEnabling provenance interoperability by analyzing heterogeneous provenance information from different scientific workflow management systems is a novel research topic. With the advent of the ProvONE model, it is now possible to model both the prospective as well as the retrospective provenance in a single provenance model. Scientific workflows are composed using a declarative definition language, such as BPEL, SCUFL/t2flow, or MoML. Associated with the execution of a workflow is its corresponding provenance that is modeled and stored in the data model specified by the workflow system. However, sharing of provenance generated by heterogeneous workflows is a challenging task and prevents the aggregate analysis and comparison of workflows and their associated provenance. To address these challenges, this paper introduces a ProvONE-based Provenance Interoperability Framework that completely automates the modeling of provenance from heterogeneous WfMSs by: (a) automatically translating the scientific workflows to their equivalent representation in a ProvONE prospective graph using the Prov2ONE algorithm, (b) enriching the ProvONE prospective graph with the retrospective provenance exported by the WfMSs, and (c) native support for storing the ProvONE provenance graphs in a Resource Description Framework triplestore that supports the SPARQL query language for querying and retrieving ProvONE graphs. The Prov2ONE algorithm is based on a set of vocabulary translation rules between workflow specifications and the ProvONE model. The correctness and completeness proof of the algorithm is shown and its complexity is analyzed. Moreover, to demonstrate the practical applicability of the complete framework, ProvONE graphs for workflows defined in BPEL, SCUFL, and MoML are generated. Finally, the provenance challenge queries are extended with six additional queries for retrieving the provenance modeled in ProvONE.