Xu Y., Päivärinta T., Kuvaja P. (2020) Digital Twins as Software and Service Development Ecosystems in Industry 4.0: Towards a Research Agenda. In: Tian Y., Ma T., Khan M. (eds) Big Data and Security. ICBDS 2019. Communications in Computer and Information Science, vol 1210. Springer, Singapore. https://doi.org/10.1007/978-981-15-7530-3_5
Digital twins as software and service development ecosystems in industry 4.0 : towards a research agenda
|Author:||Xu, Yueqiang1; Päivärinta, Tero2; Kuvaja, Pasi2|
1Martti Ahtisaari Institute, Pentti Kaiteran katu 1, FI-90014 University of Oulu, Finland
2M3S, Empirical Software Engineering on Software, Systems, and Services Faculty of Information Technology and Electrical Engineering, Pentti Kaiteran katu 1, FI-90014 University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020111890991
|Publish Date:|| 2020-11-18
While research on digital twins of cyber-physical systems within industry 4.0 is emerging, the software development perspective on digital twins remains under-explored. Contemporary definitions and examples of digital twins have covered company- or product-specific solutions or discussed the use of digital twins in rather proprietary value chains. This paper addresses the importance of taking an ecosystem view on software development on digital twins for industry 4.0 and outlines a framework for building a research agenda for such ecosystems. The framework includes three dimensions: scope of the digital twin software platform (internal, value chain, ecosystem), life-cycle phases of the industry 4.0 system related with the digital twin (creation, production, operation & maintenance, disposal), and level of integration between the twin and the physical system (model, shadow, twin). As this research-in-progress addresses examples of research questions in light of the framework, further research to build a full-scale research agenda based on a systematic literature review is suggested.
Communications in computer and information science
|Pages:||51 - 64|
Big Data and Security. ICBDS 2019
|Host publication editor:||
Khan, M. K.
Big Data and Security
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This research has been partially funded by the ITEA3 project OXILATE (https://itea3.org/project/oxilate.html).
© Springer Nature Singapore Pte Ltd. 2020. This is a post-peer-review, pre-copyedit version of an article published in Big Data and Security. ICBDS 2019. The final authenticated version is available online at: https://doi.org/10.1007/978-981-15-7530-3_5.