EA Blueprint: An Architectural Pattern for Resilient Digital Twin of the OrganizationShow others and affiliations
2021 (English)In: Dependable Computing - EDCC 2021 Workshops. / [ed] Adler R. et al, Springer, 2021, p. 120-131Conference paper, Published paper (Refereed)
Abstract [en]
Advancements in Cyber-Physical Systems, IoT, Data-driven methods, and networking prepare the adequate infrastructure for constructing new organizations, where everything is connected and interact with each other. A Digital Twin of the Organization (DTO) exploits these infrastructures to provide an accurate digital representation of the organization. Beyond the usual representation of devices, machines and physical assets supplied by Digital Twins, a DTO also include processes, services, people, roles, and all other relevant elements for the operation of organizations. Therefore, DTO can play a key role in realizing and analyzing aspects of organizations, assisting managers on the knowledge of the organization status, and foreseeing possible effects of potential changes in the organization. However, due to the dynamic, open, and ever-changing environment of organizations, an established DTO will soon degrade or even lose all its utility. Therefore, a DTO needs to evolve to recover its utility when the organization changes. The development of flexible, resilient, and easy to evolve DTO has not been well-addressed yet. Most of the existing proposals are context-dependent, system-specific, or focus on providing solutions in high-level abstraction. This work leverages Enterprise Architecture to propose an architectural pattern to serve as a blueprint toward the development of resilient DTO.
Place, publisher, year, edition, pages
Springer, 2021. p. 120-131
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1462
Keywords [en]
Resilient Digital Twin of Organization, Enterprise architecture, Architectural pattern
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-107110DOI: 10.1007/978-3-030-86507-8_12Scopus ID: 2-s2.0-85115446405ISBN: 9783030865078 (electronic)ISBN: 9783030865061 (print)OAI: oai:DiVA.org:lnu-107110DiVA, id: diva2:1596957
Conference
DREAMS, DSOGRI, SERENE 2021, Munich, Germany, September 13, 2021
2021-09-232021-09-232024-08-28Bibliographically approved