In long-lasting scientific workflow executions in HPC machines, computational scientists (the
users in this work) often need to fine-tune several workflow parameters. These tunings are …

Many scientific workflows are data-intensive and need be iteratively executed for large input
sets of data elements. Reducing input data is a powerful way to reduce overall execution …

Scientific workflows need to be iteratively, and often interactively, executed for large input
datasets. Reducing data from input datasets is a powerful way to reduce overall execution …

In extreme-scale computing environments such as the DOE Leadership Computing Facilities
scientific workflows are routinely used to coordinate software processes for the execution of …

When executing scientific workflows, anomalies of the workflow behavior are often caused
by different issues such as resource failures at the underlying infrastructure. The provenance …

Large-scale experiments in computational science are complex to manage. Due to its
exploratory nature, several iterations evaluate a large space of parameter combinations …

Repeated executions of resource-intensive workflows over a large number of runs are
commonly observed in e-science practice. We explore the hypothesis that, in some cases …

A major issue during scientific workflow execution is how to manage the large volume of
data to be processed. This issue is even more complex in cloud computing where all …

In scientific workflows, provenance data helps scientists in understanding, evaluating and
reproducing their results. Provenance data generated at runtime can also support workflow …

Dynamic workflows are scientific workflows to support computational science simulations,
typically using dynamic processes based on runtime scientific data analyses. They require …