Ontology-Driven Provenance Management in eScience: An Application in Parasite Research

Satya S. Sahoo¹, D. Brent Weatherly², Raghava Mutharaju¹, Pramod Anantharam¹, Amit Sheth¹, and Rick L. Tarleton²

¹Kno.e.sis Center., Computer Science amd Engineering Department, Wright State University, Dayton, OH 45435 USA
sahoo.2@wright.edu
mutharaju.2@wright.edu
anantharam.2@wright.edu
amit.sheth@wright.edu

²Tarleton Research Group, CTEGD, Univeristy of Georgia, Athens, GA 30602, USA
dbrentw@uga.edu
tarleton@uga.edu

Abstract. Provenance, from the French word “provenir”, describes the lineage or history of a data entity. Provenance is critical information in scientific applications to verify experiment process, validate data quality and associate trust values with scientific results. Current industrial scale eScience projects require an end-to-end provenance management infrastructure. This infrastructure needs to be underpinned by formal semantics to enable analysis of large scale provenance information by software applications. Further, effective analysis of provenance information requires well-defined query mechanisms to support complex queries over large datasets. This paper introduces an ontology-driven provenance management infrastructure for biology experiment data, as part of the Semantic Problem Solving Environment (SPSE) for Trypanosoma cruzi (T.cruzi). This provenance infrastructure, called T.cruzi Provenance Management System (PMS), is underpinned by (a) a domain-specific provenance ontology called Parasite Experiment ontology, (b) specialized query operators for provenance analysis, and (c) a provenance query engine. The query engine uses a novel optimization technique based on materialized views called materialized provenance views (MPV) to scale with increasing data size and query complexity. This comprehensive ontology-driven provenance infrastructure not only allows effective tracking and management of ongoing experiments in the Tarleton Research Group at the Center for Tropical and Emerging Global Diseases (CTEGD), but also enables researchers to retrieve the complete provenance information of scientific results for publication in literature.

Keywords: Provenance management framework, provenir ontology, Parasite Experiment ontology, provenance query operators, provenance query engine, eScience, Bioinformatics, T.cruzi parasite research

LNCS 5871, p. 992 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2009