What-If Analysis and Debugging Using Provenance Models of Scientific Workflows

Abstract

Scientific papers are the results of complex experiments done using large datasets. A researcher reading a scientific paper will not totally comprehend the ideas without learning the steps of the experiment and understanding the dataset. As this is an accepted fact, the idea of including the experimental work while publishing scientific papers has been around for many years. First, the steps were written as computer scripts and data was distributed assuming that all scientists were skilled programmers with intensive computer knowledge. Since this was not an efficient solution, the idea of scientific workflows arose. Scientific workflows illustrate the experimental steps taken to produce the scientific papers and provenance models capture a complete description of evaluation of a workflow. As provenance is crucial for scientific workflows to support reproducibility, debugging and result comprehension, they have been an increasingly important part of scientific workflows. In our paper, we argue that scientific workflow systems should support what-if analysis and debugging in order to allow users do modifications, see the results without actually running the workflow steps and be able to debug the workflows.