Yogesh L. Simmhan, Beth Plale, and Dennis Gannon
Provenance metadata in e-Science captures the derivation history of data products generated from scientific workflows. Provenance forms a glue linking workflow execution with associated data products, and finds use in determining the quality of derived data, tracking resource usage, and for verifying and validating scientific experiments. In this article, we discuss the scope of provenance collected in the Karma provenance framework used in the LEAD Cyberinfrastructure project, distinguishing provenance metadata from generic annotations. We further describe our approaches to querying for different forms of provenance in Karma in the context of queries in the first provenance challenge. We use an incremental, building-block method to construct provenance queries based on the fundamental querying capabilities provided by the Karma service centered on the provenance data model. This has the advantage of keeping the Karma service generic and simple, and yet supports a wide range of queries. Karma successfully ans
|Published in||Concurrency and Computation: Practice and Experience|
|Publisher||John Wiley & Sons Ltd.|
Bin Cao, Beth Plale, Girish Subramanian, Ed Robertson, and Yogesh Simmhan. Provenance Information Model of Karma Version 3, IEEE, July 2009.