Ontology-based industrial data management platform
Sergey Gorshkov, Alexander Grebeshkov, Roman Shebalov

TL;DR
This paper presents a hybrid data management platform that combines fast storage engines with ontology-based tools, enabling efficient handling of large, complex, and dynamic industrial data sets through a multi-model abstraction layer.
Contribution
It introduces a novel system that integrates fast storage engines with ontology-based data management, supporting SPARQL queries and SHACL constraints for industrial data.
Findings
Supports large industrial data warehouses from multiple sources.
Enables SPARQL querying over non-RDF storage.
Incorporates SHACL constraints and rules for data validation.
Abstract
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.
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Taxonomy
TopicsSemantic Web and Ontologies · Business Process Modeling and Analysis · Service-Oriented Architecture and Web Services
