Workflow/Process Management

As scientific research becomes more data intensive the concept of data science research becomes essential for scientific progress. Research artifacts such as raw data, datasets, workflows, processes, models, software tools, etc., must be seen as a whole, fully integrated amongst research communities, preserved and managed by digital means and ecosystems in an Internet/Web scale. This view is nowadays widely accepted by scientists and researchers in all areas of knowledge, and several ongoing global scale initiatives are paving the way to turn this view into common practice in the years to come.

This research thread aims at progressing the state of the art in scientific workflow management systems, with emphasis on the use of visual programming paradigms, multicriteria decision support systems, knowledge management and learning from complex network data.

This research thread development is supported by the expertise of the research group members, collaborator entities research initiatives and the DataScience4NP project (2016-2019).

Workflow management patterns, process variability modeling, multicriteria decision support, semantic web techniques, machine learning, big data analytics.