  Numerical algorithms and computational tools are instrumental in navigating
and addressing complex simulation and data processing tasks. The exponential
growth of metadata and parameter-driven simulations has led to an increasing
demand for automated workflows that can replicate computational experiments
across platforms. In general, a computational workflow is defined as a
sequential description for accomplishing a scientific objective, often
described by tasks and their associated data dependencies. If characterized
through input-output relation, workflow components can be structured to allow
interchangeable utilization of individual tasks and their accompanying
metadata. In the present work, we develop a novel computational framework,
namely, MaRDIFlow, that focuses on the automation of abstracting meta-data
embedded in an ontology of mathematical objects. This framework also
effectively addresses the inherent execution and environmental dependencies by
incorporating them into multi-layered descriptions. Additionally, we
demonstrate a working prototype with example use cases and methodically
integrate them into our workflow tool and data provenance framework.
Furthermore, we show how to best apply the FAIR principles to computational
workflows, such that abstracted components are Findable, Accessible,
Interoperable, and Reusable in nature.
