Data Science Workflows for the Cloud/Edge Computing Continuum

Abstract

Research infrastructures play a crucial role in the development of data science. In fact, the conjunction of data, infrastructures and analytical methods enable multidisciplinary scientists and innovators to extract knowledge and to make the knowledge and experiments reusable by the scientific community, innovators providing an impact on science and society. Resources such as data and methods, help domain and data scientists to transform research in an innovation question into a responsible data-driven analytical. On the other hands, Edge computing is a new computing paradigm that is spreading and developing at an incredible pace. Edge computing is based on the assumption that for certain applications is beneficial to bring the computation as much close as possible to data or end-users. This paper introduces an approach for writing data science workflows targeting research infrastructures that encompass resources located at the edge of the network.