P+RProv: Prospective+Retrospective Provenance Graphs of Python Scripts

Abstract

The evolution of technology has enabled scientists to advance the automation of scientific experiments. Many programming languages have become popular in the scientific environment, especially scripting languages, due to their high abstraction level and simplicity, allowing the specification of complex tasks in fewer steps than traditional programming languages. Due to these features, lots of scientists model their scientific experiments in scripting languages to ensure data management and results control. However, this type of experiment usually generates large volumes of data, making data analysis and threat mitigation difficult. To fill in this gap, we propose P+RProv, an approach to aid scientists in understanding the structure of Python scripts and their results.