Workflows are increasingly adopted to describe large-scale data- and compute-intensive scientific simulations which leverage the wealth of distributed data sources and computing infrastructures. Nonetheless, most scientific workflow formalisms remain difficult to exploit for scientists who are neither experts nor enthusiasts of distributed computing, because they mix the scientific processes they model with their implementations, blurring the lines between what is done and how it is done, as well as between what is and what is not infrastructure-dependent. Our objective is to improve scientific workflow accessibility and ease scientific workflow design and reuse, by elevating the abstraction level, emphasizing the scientific experiment over technicalities, ensuring proper separation between functional and non-functional concerns and leveraging domain knowledge and know-how. The main contributions of this work are: (i) a multi-level structurally flexible semantic scientific workflow model, called the Conceptual Workflow Model, which lets users design simulations at a computation-independent level and focus on domain goals and methods; and (ii) a computer-assisted Transformation Process relying on knowledge engineering technologies to help users transform their high-level simulation models into executable workflow artifacts which can be delegated to third-party frameworks for enactment.
