Managing Provenance of Implicit Data Flows in Scientific Experiments

Abstract

Scientific experiments modeled as scientific workflows may create, change, or access data products not explicitly referenced in the workflow specification, leading to implicit data flows. The lack of knowledge about implicit data flows makes the experiments hard to understand and reproduce. In this article, we present ProvMonitor, an approach that identifies the creation, change, or access to data products even within implicit data flows. ProvMonitor links this information with the workflow activity that generated it, allowing for scientists to compare data products within and throughout trials of the same workflow, identifying side effects on data evolution caused by implicit data flows. We evaluated ProvMonitor and observed that it could answer queries for scenarios that demand specific knowledge related to implicit provenance.