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Semantic Digital Twins in the Industrial Internet of Things

dc.contributor.advisorLehmann, Jens
dc.contributor.authorBader, Sebastian Richard
dc.date.accessioned2022-06-20T12:31:41Z
dc.date.available2022-06-20T12:31:41Z
dc.date.issued20.06.2022
dc.identifier.urihttps://hdl.handle.net/20.500.11811/9884
dc.description.abstractThe seamless connection of control devices, physical assets, and IT systems through internet-based networks is one of the current megatrends. Especially the manufacturing industry experiences disruptive changes in how products are designed, documented, and provided to customers. The appropriate digital representation of products becomes a must-have for future competitiveness, and the standardized integration with arbitrary third-party applications is no longer an additional feature but strictly demanded by customers.
While the general potential is without question, the recent activities have brought specific challenges to the surface. Consistent and industry-wide data modeling, standardized and reliable data exchange mechanisms, and distributed and scalable communication architectures have not yet been achieved. Limited attempts with small numbers of partners and controlled domains have implemented working prototypes, but an industry-wide, comprehensive approach is still not achieved.
Combining formal data modeling paradigms of semantic technologies with reliable and well-known internet technologies forms the foundation of this work. The concept of Digital Twins is extended and specified to consistent and self-declarative entities imposing the atomic building blocks for the integration of components, facilities, and applications across networks, companies, and domains. Based on proven technologies, widely accepted conventions, and new extensions, new patterns are outlined to overcome six identified key challenges: the unpredictable requirements of IIoT settings, their Brownfield nature, the communication between heterogeneous devices, the non-transparent design decisions of the device interfaces, the dynamic changes in the networks, and the need to keep the sovereignty of the involved data.
This thesis contributes to the state of the art by examining the communication between heterogeneous assets of any kind and analyzing how the recommended patterns can be transferred into the so-called Industrial Internet of Things. In particular, it presents (1) how the assets themselves need to be modeled to become suitable, broadly usable Digital Twins, (2) which interactions they need to provide and how they have to be consumed by legacy and future services, and (3) how the flexible ecosystems of tomorrow's manufacturing processes look like and how the current standardization landscape supports and defines it.
The insights of the presented work have been incorporated in several research publications and used in various standardization activities to perpetuate the outlined approaches, namely the frameworks and technical specifications of the International Data Spaces (IDS), the Plattform Industrie 4.0, and the upcoming Gaia-X Cloud Ecosystems.
en
dc.language.isoeng
dc.rightsIn Copyright
dc.subjectIndustrial Internet of Things
dc.subjectDigitaler Zwilling
dc.subjectSemantic Web
dc.subjectIndustrie 4.0
dc.subjectLinked Data
dc.subjectDigital Twin
dc.subjectIndustry 4.0
dc.subject.ddc004 Informatik
dc.titleSemantic Digital Twins in the Industrial Internet of Things
dc.typeDissertation oder Habilitation
dc.publisher.nameUniversitäts- und Landesbibliothek Bonn
dc.publisher.locationBonn
dc.rights.accessRightsopenAccess
dc.identifier.urnhttps://nbn-resolving.org/urn:nbn:de:hbz:5-66298
ulbbn.pubtypeErstveröffentlichung
ulbbnediss.affiliation.nameRheinische Friedrich-Wilhelms-Universität Bonn
ulbbnediss.affiliation.locationBonn
ulbbnediss.thesis.levelDissertation
ulbbnediss.dissID6629
ulbbnediss.date.accepted01.04.2022
ulbbnediss.instituteMathematisch-Naturwissenschaftliche Fakultät : Fachgruppe Informatik / Institut für Informatik
ulbbnediss.fakultaetMathematisch-Naturwissenschaftliche Fakultät
dc.contributor.coRefereeMaleshkova, Maria
ulbbnediss.contributor.orcidhttps://orcid.org/0000-0003-1328-704X
ulbbnediss.contributor.gnd1260495574


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