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Researchers are becoming more effective in their work with the automation of scientific discovery processes and experiments in dry-labs rather than through wet-labs experiments. The automation of these processes requires complex workflows, which is one of the promising techniques to help collaborating scientists to share their experimental results and achieve them via a distributed software infrastructure. Subsequently, the history of the findings needs to be recorded - so called provenance - in order to be reproducible and re-provable. Various solutions and techniques have been elaborated for provenance data collection and analysis. In this paper we propose a taxonomy to categorize existing solutions for run-time and execution provenance of workflows into workflow enactment engine dependent, so called listener mode provenance category, and enactment engine independent, so called monitoring mode provenance category. Additionally, our novel solution for a monitoring mode provenance concept at the middleware level is introduced and the design, architecture and performance evaluation of our prototype implementation are discussed.

Enactment engine independent provenance recording for e-science infrastructures