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RDFProv: A Relational RDF Store for Querying and Managing Scientific Workflow Provenance (2010)
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BibTeX
@MISC{Chebotko10rdfprov:a,
author = {Artem Chebotko and Shiyong Lu and Xubo Fei and Farshad Fotouhi},
title = {RDFProv: A Relational RDF Store for Querying and Managing Scientific Workflow Provenance},
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, that 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.
Keyphrases
relational rdf store managing scientific workflow provenance scientific workflow provenance management provenance metadata relational database schema production quality real-life biological simulation workflow commercial native rdf store rdfprov system input provenance ontology result interpretation semantic web technology case study provenance rdf metadata experimental result general rdf data management improved performance problem diagnosis efficient data type information scientific workflow provenance querying provenance management inference advantage schema-independent sparql-to-sql translation algorithm scientific workflow environment provenance metadata management popular relational rdf store scientific discovery reproducibility owl provenance ontology common provenance query provenance management problem
