Provenance that captures e-Science activity has long term value only if the right amount and kind of information is collected. In this paper, we propose a two-layer model for representing provenance information capable of representing both execution information and higher level process details. The information model forms the basis for efficient relational database storage and query, and sets the stage for investigation of the necessary and sufficient information for long-term preservation.

In: International Workshop on Scientific Workflows (SWF)

Publisher: IEEE
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Previous Versions

Yogesh L. Simmhan, Beth Plale, and Dennis Gannon. A Framework for Collecting Provenance in Data-Centric Scientific Workflows, January 2006.

Yogesh Simmhan, Beth Plale, and Dennis Gannon. A Survey of Data Provenance in e-Science, SIGMOD Record, ACM Press, September 2005.

Yogesh L. Simmhan, Beth Plale, and Dennis Gannon. Karma2: Provenance Management for Data-Driven Workflows, International Journal of Web Services Research (JWSR), IGI Publishing, April 2008.

Yogesh L. Simmhan, Beth Plale, Dennis Gannon, and Suresh Marru. Performance Evaluation of the Karma Provenance Framework for Scientific Workflows, Springer, 2006.

Yogesh L. Simmhan, Beth Plale, and Dennis Gannon. Query capabilities of the Karma provenance framework, Concurrency and Computation: Practice and Experience, John Wiley & Sons Ltd., April 2008.

Luc Moreau, Beth Plale, Simon Miles, Carole Goble, Paolo Missier, Roger Barga, Yogesh Simmhan, Joe Futrelle, Robert McGrath, Jim Myers, Patrick Paulson, Shawn Bowers, Bertram Ludaescher, Natalia Kwasnikowska, Jan Van den Bussche, Tommy Ellkvist, Juliana Freire, and Paul Groth. The Open Provenance Model (v1.01), July 2008.