Ontology-based industrial data management platform
This addresses data management challenges for industrial applications, but it appears incremental as it integrates existing technologies rather than introducing a fundamentally new approach.
The paper tackles the problem of managing large industrial data warehouses from diverse sources by combining fast storage engines with ontology-based tools, resulting in a system that supports SPARQL queries and SHACL constraints.
Relational and noSQL storages are developed for the fast processing of the large data sets having a stable structure, while the ontologies are used to rep-resent complex and dynamic sets of information of a limited size. In the in-dustrial applications it is often needed to maintain the large warehouses of data consolidated from various sources. The ontologies are useful to repre-sent the structure of that data, but RDF triple stores are not well suitable for storing it. We offer an approach and a system allowing to use the opportuni-ties of fast storage engines along with the flexibility of ontology-based data management tools, including SPARQL queries. The system implements a multi-model data abstraction layer which allows working with the data as if it is situated in RDF triple store, executes SPARQL queries over it and ap-plies SHACL constraints and rules.