Provenance Analytics for Workflow-Based Computational Experiments

Abstract

Until not long ago, manually capturing and storing provenance from scientific experiments were constant concerns for scientists. With the advent of computational experiments (modeled as scientific workflows) and Scientific Workflow Management Systems, produced and consumed data, as well as the provenance of a given experiment, are automatically managed, so provenance capturing and storing in such a context is no longer a major concern. Similarly to several existing big data problems, the bottom line is now on how to analyze the large amounts of provenance data generated by workflow executions and how to be able to extract useful knowledge of this data. In this context, this article surveys the current state of the art on provenance analytics by presenting the key initiatives that have been taken to support provenance data analysis. We also contribute by proposing a taxonomy to classify elements related to provenance analytics.