A Systematic Comparison of Semantic Integration Data Storage Architectures for Multidisciplinary Systems

Abstract

Multidisciplinary projects typically rely on the contributions of various disciplines using heterogeneous engineering tools. This paper focuses on the challenge of querying across different disciplines, which may be influenced by the selection of a proper instance data storage architecture for storing the heterogeneous tool data. Specifically, we have identified three different architectures: ontology file stores, triple stores and relational database stores. This paper systematically compares these architectures using an industrial case study and analyses their selection according to important requirements such as performance and maintainability.