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Scientific workflows are increasingly used for rapid integration of existing algorithms to form larger and more complex programs. Such workflows promise to provide more abstract, yet executable views of data analyses through graphs of components representing computational tasks and edges representing the data-flow between tasks. However, designing workflows using purely data-flow-oriented models of computation introduces a number of challenges, including the need to introduce low-level components to mediate and transform data (so-called shims) and a large number of additional "Wires" for routing data to components within a workflow.
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