UMDISW: A Universal Multi-Domain Intelligent Scientific Workflow Framework for the Whole Life Cycle of Scientific Data

Abstract

Existing scientific data management systems rarely manage scientific data from a whole-life-cycle perspective, and the value-creating steps defined throughout the cycle constitute essentially a scientific workflow. The scientific workflow system developed by many organizations can well meet their own domain-oriented needs, but from the perspective of the entire scientific data, there is a lack of a common framework for multiple domains. At the same time, some systems require scientists to understand the underlying content of the system, which virtually increases the workload and research costs of scientists. In this context, this paper proposes a universal multi-domain intelligent scientific data processing workflow framework (UMDISW), which builds a general model that can be used in multiple domains by defining directed graphs and descriptors, and makes the underlying layer transparent to scientists to just focus on high-level experimental design. On this basis, the paper also uses scientific data as a driving force, incorporating a mechanism of intelligently recommending algorithms into the workflow to reduce the workload of scientific experiments and provide decision support for exploring new scientific discoveries.