Provenance Support for Grid-Enabled Scientific Workflows

The Grid is evolving and new concepts like Semantic Grid, Knowledge Grid are rapidly emerging, where humans and distributed machines share, exchange, and manage data and resources intelligently. Computational scientists typically use workflows to describe and manage scientific discovery processes. However, the credibility of the obtained results in the scientific community is questionable if the computational experiment is not reproducible. This issue is being addressed in our research reported in this paper via development of workflow provenance system for Grid-enabled scientific workflows. Workflow provenance collects data on workflow activities, data flow and workflow clients. Provenance information can be used to trace and test workflows and the data produced. Our approach supports reproducibility (i.e. to support re-enactment of workflow by an independent user) and dataflow visualization (i.e. visualization of statistical characteristics of input/output data). We illustrate our approach on the Non-Invasive Glucose Measurement (NIGM) application.