CALL FOR PAPERS

Data-intensive workflows (a.k.a. scientific workflows) are routinely used in most scientific disciplines today, especially in the context of high-performance, parallel and distributed computing. They provide a systematic way of describing a complex scientific process and rely on sophisticated workflow management systems to execute on a variety of parallel and distributed resources. With the dramatic 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, being at the interface between end-users and computing infrastructures.

This workshop focuses on the many facets of data-intensive workflow management systems, ranging from actual execution to service management and the coordination and optimization of data, service and job dependencies. The workshop 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 to optimize the execution of the workflow for different infrastructures; 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, scheduling and fault detection and tolerance.

The topics of the workshop include but are not limited to:

Big Data analytics workflows
Data-driven workflow processing (including stream-based workflows)
Workflow composition, tools, and languages
Workflow execution in distributed environments (including HPC, clouds, and grids)
Reproducible computational research using workflows
Dynamic data dependent workflow systems solutions
Exascale computing with workflows
In Situ Data Analytics Workflows
Interactive workflows (including workflow steering)
Workflow fault-tolerance and recovery techniques
Workflow user environments, including portals
Workflow applications and their requirements
Adaptive workflows
Workflow optimizations (including scheduling and energy efficiency)
Performance analysis of workflows
Workflow debugging
Workflow provenance
Workflows in constrained environments e.g. IoT, Edge computing, etc.

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