On The Use Of Semantic Abstract Workflows Rooted On Provenance Concepts

Two challenges related to capturing provenance about scientific data are: 1) determining an adequate level of granularity to encode provenance, and 2) encoding provenance in a way that facilitates end-user interpretation and analysis. A solution to address these challenges consists in integrating two technologies: Semantic Abstract Workflows (SAWs), which are used to capture a domain expert's understanding of a scientific process, and PML, an extensible language used to encode provenance. This paper describes relevant features of these technologies for addressing the granularity and interpretation challenges of provenance encoding and presents a discussion about their integration.