—Scientific workflows have recently emerged as a new paradigm for representing and managing complex distributed scientific computations and are used to accelerate the pace of scientific discovery. In many disciplines, individual workflows are large due to the large quantities of data used. As scientific workflows scale quickly, they become very hard to build and maintain. Recent efforts from scientific workflow community aiming at large-scale capturing of provenance present a new opportunity for building scientific workflows using provenance. Process mining focusses on extracting information about processes by examining event logs, and has been successfully applied to business workflow management. This paper presents a method using process mining based on provenance to build and analyze scientific workflows, which provides a new direction in using captured provenance.
