Modern scientific collaborations have opened up the opportunity to solve complex problems
that require both multidisciplinary expertise and large-scale computational experiments …

Interest in processing big data has increased rapidly to gain insights that can transform
businesses, government policies, and research outcomes. This has led to advancement in …

Automation of the execution of computational tasks is at the heart of improving scientific
productivity. Over the last years, scientific workflows have been established as an important …

This special issue and our editorial celebrate 10 years of progress with data-intensive or
scientific workflows. There have been very substantial advances in the representation of …

Computational workflows describe the complex multi-step methods that are used for data
collection, data preparation, analytics, predictive modelling, and simulation that lead to new …

Workflows are among the most commonly used tools in a variety of execution environments.
Many of them target a specific environment; few of them make it possible to execute an …

Workflow and serverless frameworks have empowered new approaches to distributed
application design by abstracting compute resources. However, their typically limited or one …

Parallel and multiprocessing algorithms break down significant numerical problems into
smaller subtasks, reducing the total computing time on multiprocessor and multicore …

Big Data processing, especially with the increasing proliferation of Internet of Things (IoT)
technologies and convergence of IoT, edge and cloud computing technologies, involves …

Smartphone-based assessments have been considered a potential solution to passively
monitor gait and mobility in early-stage Parkinson's disease (PD) patients. In the Multiple …