Data-Intensive Workflows (a.k.a. scientific workflows) are routinely used in most scientific disciplines today, especially in the context of parallel and distributed computing. Workflows provide a systematic way of describing the analysis, and rely on workflow management systems to execute the complex analyses on a variety of distributed resources. They are at the interface of end-users and computing infrastructures. With the drastic increase of raw data volume in every domain, they play an even more critical role to assist scientists in organizing and processing their data and to leverage HPC or HTC resources.

This workshop focuses on the many facets of data-intensive workflow management systems, ranging from job execution to service management and the coordination of data, service, and job dependencies. The workshop therefore covers a broad range of issues in the scientific workflow lifecycle that include: data-intensive workflows representation and enactment; designing workflow composition interfaces; workflow mapping techniques that may optimize the execution of the workflow; workflow enactment engines that need to deal with failures in the application and execution environment; and a number of computer science problems related to scientific workflows such as semantic technologies, compiler methods, fault detection, and tolerance.