  With recent increasing computational and data requirements of scientific
applications, the use of large clustered systems as well as distributed
resources is inevitable. Although executing large applications in these
environments brings increased performance, the automation of the process
becomes more and more challenging. While the use of complex workflow management
systems has been a viable solution for this automation process in business
oriented environments, the open source engines available for scientific
applications lack some functionalities or are too difficult to use for
non-specialists. In this work we propose an architectural model for a grid
based workflow management platform providing features like an intuitive way to
describe workflows, efficient data handling mechanisms and flexible fault
tolerance support. Our integrated solution introduces a workflow engine
component based on ActiveBPEL extended with additional functionalities and a
scheduling component providing efficient mapping between tasks and available
resources.
