Scientific Workflow, Provenance, and Data Modeling Challenges and Approaches

Abstract

Semantic modeling approaches (e.g., conceptual models, controlled vocabularies, and ontologies) are increasingly being adopted to help address a number of challenges in scientific data management. While semantic information has played a considerable role within bioinformatics, semantic technologies can similarly benefit a wide range of scientific disciplines. Here we focus on three main areas where modeling and semantics are playing an increasingly important role: scientific workflows, scientific data provenance, and observational data management. Applications of these areas span a number of disciplines and provide both challenges and new opportunities for conceptual modeling research and development. We provide a brief overview of each area, discuss the role that modeling plays within each, and present current research opportunities.