Querying and managing opm-compliant scientific workflow provenance

<p>Provenance, the metadata that records the derivation history of scientific results, is important in scientific workflows to interpret, validate, and analyze the result of scientific computing. Recently,</p> <p>to promote and facilitate interoperability among heterogeneous provenance systems, the Open Provenance Model (OPM) has been proposed and has played an important role in the community.</p> <p>In this dissertation, to efficiently query and manage OPM-compliant provenance, we first propose a provenance collection framework that collects both prospective provenance, which captures</p> <p>an abstract workflow specification as a recipe for future data derivation and retrospective provenance, which captures past workflow execution and data derivation information. We then</p> <p>propose a relational database-based provenance system, called OPMPROV that stores, reasons, and queries prospective and retrospective provenance, which is OPM-compliant provenance. We finally propose OPQL, an OPM-level provenance query language, that is directly defined over the OPM model. An OPQL query takes an</p> <p>OPM graph as input and produces an OPM graph as output; therefore, OPQL queries are not tightly coupled to the underlying provenance storage strategies. Our provenance store, provenance collection framework, and provenance query language feature the native support of the OPM model.</p>

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Open Access Dissertation

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2012

Author dc:creator

• Lim, Chunhyeok

Contributors dc:contributor

• Farshad Fotouhi
• Shiyong Lu

• OPM, OPM-Compliant Provenance, OPMProv, OPQL, Provenance, Scientific Workflow
• Computer Engineering
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.wayne.edu/oa_dissertations/382

OAI identifier oai:identifier

oai:digitalcommons.wayne.edu:oa_dissertations-1381