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Workflow management has been widely adopted by scientific communities as a valuable tool to carry out complex experiments. It allows for the possibility to perform computations for data analysis and simulations, whereas hiding details of the complex infrastructures underneath. There are many workflow management systems that offer a large variety of generic services to coordinate the execution of workflows. Nowadays, there is a trend to extend the functionality of workflow management systems to cover all possible requirements that may arise from a user community. However, there are multiple scenarios for usage of workflow systems, involving various actors that require different services to be supported by these systems. In this paper we reflect about the usage scenarios of scientific workflow management based on the practical experience of heavy users of workflow technology from communities in three scientific domains: Astrophysics, Heliophysics and Biomedicine. We discuss the requirements regarding services and information to be provided by the workflow management system for each usage profile, and illustrate how these requirements are fulfilled by the tools these communities currently adopt. This paper contributes to the understanding of properties of future workflow management systems that are important to increase their adoption in a large variety of usage scenarios.

This paper presents the integration of a workflow management system into the VL-e medical software architecture. Workflows are designed with the Taverna workbench, and then executed with the MOTEUR engine on the EGEE grid through the VBrowser, which is the basic front-end for grid-enabled applications in the VL-e medical project. Data management is handled by the virtual file system of the VL-e toolkit. The resulting system provides a high-level interface to execute grid applications.

Diffusion Tensor MRI (DTI) is a rather recent image acquisition modality that can help identify disease processes in nerve bundles in the brain. Due to the large and complex nature of such data, its analysis requires new and sophisticated pipelines that are more efficiently executed within a grid environment. We present our progress over the past four years in the development and porting of the DTI analysis pipeline to grids. Starting with simple jobs submitted from the command-line, we moved towards a workflow-based implementation and finally into a web service that can be accessed via web browsers by end-users. The analysis algorithms evolved from basic to state-of-the-art, currently enabling the automatic calculation of a population-specific ‘atlas’ where even complex brain regions are described in an anatomically correct way. Performance statistics show a clear improvement over the years, representing a mutual benefit from both a technology push and application pull.

Grid workflows can produce thousands of results that should be properly organised to enable further analysis. Typically results are stored on locations hard-coded in the workflow or in the components, limiting reusability. In this paper we present an approach to (re)organise the output files generated by a grid workflow in a distributed storage environment. We propose to perform a post-mortem mapping of workflow results into a directory structure. This mapping is based on data provenance information and exploits grid catalog features, namely logical file names, to avoid data replication. By defining different mappings, users can generate their own semantic view of results generated during a workflow execution, which fosters user-friendliness, whereas preserving workflow reusability. An implementation on the Virtual Resource Browser (VBrowser) framework is detailed and evaluated on neuroimaging workflows. Results show that the complex directory structure of an image analysis application cane properly generated by our system. An initial performance evaluation of the mapping resolution and directory structure creation indicates that this approach provides a practical, simple, yet powerful solution to an important roadblock for the adoption of workflows to implement complex image analysis pipelines.