Semantic service discovery in heterogeneous cyber-physical systems

Andreas Lober, Hartwig Baumgärtel and Richard Verbeet

A chapter in Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain, 2020, pp 591-623 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: Purpose: A primary requirement of Industry 4.0 and realization of Cyber-Physical Systems in production and logistics is the dynamic connection of physical and digital components. Service-oriented Architectures are a well established approach to meet this requirement. However, a service discovery using syntactic descriptions of services limits efficient application of a Service-oriented Architecture concerning the complexity and variability of existing processes. Methodology: A semantics based mechanism for service discovery can solve this limitation. It uses an ontology management system containing a domain specific ontology and modelling specific Cyber-Physical Systems as individuals. SPARQL Protocol And RDF Query Language (SPARQL) queries searching this ontology with context-related parameters. A use case demonstrates the mechanism by realizing an in-house transport request. Findings: A syntax based service discovery requires a definition and publishing of unique service names. However, complex Cyber-Physical Systems using multiple parameters during service calls require disproportionate effort to implement and maintain these names. A semantics based service discovery considers various parameters by using a specific ontology calling services by their properties without knowing the service name. Originality: A semantics based is decoupled from specific service implementations of components in a Cyber-Physical Systems. Therefore, an explicit specification of parameter configurations in service descriptions is not necessary. A Service-oriented Architecture can be implemented in complex systems without extensive adjustments or coordination mechanisms.

Keywords: Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:228934

DOI: 10.15480/882.3125

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