RDFProv: A relational RDF store for querying and managing scientific workflow provenance

  • Authors:

  • Artem Chebotko;Shiyong Lu;Xubo Fei;Farshad Fotouhi

  • Affiliations:

  • Department of Computer Science, University of Texas-Pan American, 1201 West University Drive, Edinburg, TX 78539, USA;Department of Computer Science, Wayne State University, 431 State Hall, 5143 Cass Avenue, Detroit, MI 48202, USA;Department of Computer Science, Wayne State University, 431 State Hall, 5143 Cass Avenue, Detroit, MI 48202, USA;Department of Computer Science, Wayne State University, 431 State Hall, 5143 Cass Avenue, Detroit, MI 48202, USA

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2010

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Abstract

Provenance metadata has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. The provenance management problem concerns the efficiency and effectiveness of the modeling, recording, representation, integration, storage, and querying of provenance metadata. Our approach to provenance management seamlessly integrates the interoperability, extensibility, and inference advantages of Semantic Web technologies with the storage and querying power of an RDBMS to meet the emerging requirements of scientific workflow provenance management. In this paper, we elaborate on the design of a relational RDF store, called RDFProv, which is optimized for scientific workflow provenance querying and management. Specifically, we propose: i) two schema mapping algorithms to map an OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) three efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable. The comparison with two popular relational RDF stores, Jena and Sesame, and two commercial native RDF stores, AllegroGraph and BigOWLIM, showed that our optimizations result in improved performance and scalability for provenance metadata management. Finally, our case study for provenance management in a real-life biological simulation workflow showed the production quality and capability of the RDFProv system. Although presented in the context of scientific workflow provenance management, many of our proposed techniques apply to general RDF data management as well.