The rapidly increasing amounts of data available from new high-throughput methods have made data processing without automated pipelines infeasible. As was pointed out in several publications, integration of data and analytic resources into workflow systems provides a solution to this problem, simplifying the task of data analysis. Various applications for defining and running workflows in the field of bioinformatics have been proposed and published, e.g. Galaxy, Mobyle, Taverna, Pegasus or Kepler. One of the main aims of such workflow systems is to enable scientists to focus on analysing their datasets instead of taking care for data management, job management or monitoring the execution of computational tasks. The currently available workflow systems achieve this goal, but fundamentally differ in their way of executing workflows.