Evaluation of semantic data storages for integrating heterogenous disciplines in automation systems engineering

Automation systems development projects typically require the integration of heterogeneous local tool data models that come from various disciplines and sources. Semantic data integration provides solutions for bridging semantic gaps between common project-level concepts and the local tool concepts used by each discipline. The following use cases represent the foundation for efficient data integration: (a) data insertion in the local tool models, (b) transformation of data between the local models and a common model, and (c) querying across concepts from different local models by using the common model. The selection of a proper semantic data storage for storing the data has a strong impact on efficiently executing these use cases. Three different important types of semantic storages have been identified: ontology file storages, triple storages, and relational databases storages. In this paper, we evaluate them, and identify their drawbacks and advantages in the context of the presented integration use cases' requirements.