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Towards a Reference Architecture for Leveraging Model Repositories for Digital Twins

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

In the area of Cyber-Physical Systems (CPS), the degree of complexity continuously increases mainly due to new key-enabling technologies supporting those systems. One way to deal with this increasing complexity is to create a digital representation of such systems, a so-called Digital Twin (DT), which virtually acts in parallel ideally across the entire life-cycle of a CPS. For this purpose, the DT uses simulated or real-time data to mimic operations, control, and may modify the CPS’s behaviour at runtime. However, building such DTs from scratch is not trivial, mainly due to the integration needed to deal with heterogeneous systems residing in different technological spaces. In order to tackle this challenge, Model-Driven Engineering (MDE) allows to logically model a CPS with its physical components. Usually, in MDE such “logical models” are created at design time which keep them detached from the deployed system during runtime. Instead of building bilateral solutions between each runtime environment and every engineering tool, a dedicated integration layer is needed which can deal with both, design and runtime aspects. Therefore, we present a reference architecture that allows on the one side to query data from model repositories to enrich the running system with design-time knowledge, and on the other side, to be able to reasoning about system states at runtime in design-time models. We introduce a model repository query and management engine as mediator and show its feasibility by a demonstration case.
OriginalspracheEnglisch
TitelETFA 2020, IEEE International Conference on Emerging Technologies and Factory Automation, Vienna, Austria, 8.-11. September 2020
Seiten1077-1080
Seitenumfang4
ISBN (elektronisch)9781728189567
DOIs
PublikationsstatusVeröffentlicht - Sep. 2020

Publikationsreihe

NameIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Band2020-September
ISSN (Print)1946-0740
ISSN (elektronisch)1946-0759

Wissenschaftszweige

  • 202017 Embedded Systems
  • 102002 Augmented Reality
  • 102006 Computer Supported Cooperative Work (CSCW)
  • 102015 Informationssysteme
  • 102020 Medizinische Informatik
  • 102022 Softwareentwicklung
  • 102034 Cyber-Physical Systems
  • 201132 Computational Engineering
  • 201305 Verkehrstechnik
  • 207409 Navigationssysteme
  • 502032 Qualitätsmanagement
  • 502050 Wirtschaftsinformatik
  • 503015 Fachdidaktik Technische Wissenschaften

JKU-Schwerpunkte

  • Digital Transformation

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