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dc.contributor.authorKamburjan, Eduard
dc.contributor.authorDin, Crystal Chang
dc.contributor.authorSchlatte, Rudolf
dc.contributor.authorTapia Tarifa, Silvia Lizeth
dc.contributor.authorJohnsen, Einar Broch
dc.date.accessioned2023-02-16T09:25:37Z
dc.date.available2023-02-16T09:25:37Z
dc.date.created2022-10-31T14:52:10Z
dc.date.issued2022
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11250/3051371
dc.description.abstractDigital twin applications use digital artefacts to twin physical systems. The purpose is to continuously mirror the structure and behavior of the physical system, such that users can analyse the physical system by means of the digital twin. However, the physical system might change over time. In this case, the digital twin’s ensemble of digital artefacts needs to be reconfigured to correctly twin the physical system again. This paper considers a digital twin infrastructure combining MAPE-K feedback loops and semantic reflection to automatically ensure that the digital artefacts correctly twin the physical system; i.e., the resulting system is twinned-by-construction. We consider the monitoring of both structural and temporal correctness properties for digital twin, including the time delay required by reconfiguration, and the capture of execution traces to reflect digital threads in the digital twin framework.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.titleTwinning-by-Construction: Ensuring Correctness for Self-adaptive Digital Twinsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright 2022 the authorsen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.doi10.1007/978-3-031-19849-6_12
dc.identifier.cristin2066938
dc.source.journalLecture Notes in Computer Science (LNCS)en_US
dc.source.pagenumber188-204en_US
dc.relation.projectNorges forskningsråd: 294600en_US
dc.relation.projectNorges forskningsråd: 237898en_US
dc.relation.projectNorges forskningsråd: 274515en_US
dc.identifier.citationLecture Notes in Computer Science (LNCS). 2022, 13701, 188-204.en_US
dc.source.volume13701en_US


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