Implicit provenance gathering through configuration management

Scientific experiments based on computer simulations usually consume and produce huge amounts of data. Data provenance is used to help scientists answer queries related to how experiment data were generated or changed. However, during the experiment execution, data not explicitly referenced by the experiment specification may lead to an implicit data flow missed by the existing provenance gathering infrastructures. This paper introduces a novel approach to gather and store implicit data flow provenance through configuration management. Our approach opens some new opportunities in terms of provenance analysis, such as identifying implicit data flows, identifying data transformations along an experiment trial, comparing data evolution in different trials of the same experiment, and identifying side effects on data evolution caused by implicit data flows.