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Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such process. Since the sequential execution of data-intensive scientific workflows may take much time, Scientific Workflow Management Systems (SWfMSs) should enable the parallel execution of data-intensive scientific workflows and exploit thedoi:10.1007/s10723-015-9329-8 fatcat:5urst5aphjftbli3pukmnbutri