MANSOR: A module alignment method based on neighbor information for scientific workflow

Finding similar scientific workflow modules that can substitute essential components of the privacy workflows from public repositories to create a personalized workflow is growing in popularity. This is a cost-effective and error-free strategy for the scientific community. Currently, module alignment approaches heavily depend on syntactic information such as modules' names and types. However, the contextual semantic information of modules, which encompasses inputs, outputs, and datalinks connecting modules, can also convey their functions. Unfortunately, this information is scarcely utilized during the module alignment process. In this work, we propose a module alignment method based on neighbor information for scientific workflow (MANSOR). Specifically, we present a rule-based attribute similarity computation approach for calculating initial module-pair similarity in two workflows. The relation similarity is then employed to iteratively fine-tune the matching degree with uncertainty of module pairs by considering the contextual semantics until reaching a steady state. After pairwise comparison of workflows, the module alignment results are determined based on their module-pair similarity. Experimental results on the human-curated corpus of ratings indicate that MANSOR outperforms the existing state-of-the-art approaches with statistical significance.