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Data-centric and service-oriented workflows are commonly used in scientific research to enable the composition and execution of complex analysis on distributed resources. Although there are a plethora of orchestration frameworks to implement workflows, most of them are unsuitable for executing (enacting) data-centric workflows since they are based on a centralized orchestration engine which can be a bottleneck when handling large data volumes. In this paper, we propose a flexible anddoi:10.1016/j.future.2013.01.008 fatcat:tpmgbwshajc6dfbhl7h73qlc5i