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A scientific workflow consists of a series of structured activities and computations that arise in scientific problem-solving. Recent work [7] has demonstrated that collection-oriented modelling and design COMAD [3] leads to simpler and more robust workflow design. In COMAD, for example, each actor is wrapped with a well defined configuration that hides the low level complexities of "wiring" processes together with respective data sources. On the other hand, some dataflow details e.g., fine-grained data dependency information are hidden in the workflow graph that the user may construct an erroneous or unoptimized workflow due to lack of information. Such problems are difficult to detect before workflow execution. Hence, configuring, maintaining and designing a collection-oriented workflow is sometimes challenging and time-consuming, in addition, large-scale workflows often tend to run for long time, therefore, error free and optimized workflow design is always desired.

Improving Workflow Design Using Abstract Provenance Graphs