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Graph Databases for Group Decision Making in Industry: A Comprehensive Literature Review
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Virtual Production Development (VPD))ORCID iD: 0000-0003-1679-3319
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Department of Civil and Industrial Engineering, Uppsala University, Sweden. (Virtual Production Development (VPD))ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Virtual Production Development (VPD))ORCID iD: 0009-0006-6208-4790
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Virtual Production Development (VPD))ORCID iD: 0000-0001-5436-2128
2025 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 13, article id 3596632Article, review/survey (Refereed) Published
Abstract [en]

Virtual manufacturing, simulation, and optimization provide a wealth of knowledge about the possibilities of future production systems so as to support decision makers. However, this knowledge usually remains with a handful of domain experts, is not captured and is hard to share even within the same team. At the same time, simulations can benefit from the incorporation of linked data from real factories once a process is running. Graph databases provide a possible approach to storing and managing this form of interrelated heterogeneous data, with powerful querying capabilities that can identify important or interesting patterns that might otherwise remain hidden. Current research focuses on one or two aspects of this problem but does not address all at once, despite the potential benefits of the combination. This paper provides a broad literature review of the current directions within research with a special focus on how graphs can support finding knowledge within Virtual Factories, used by larger teams for industrial planning and optimization.

Place, publisher, year, edition, pages
IEEE, 2025. Vol. 13, article id 3596632
Keywords [en]
Graph database, Industry 4.0, Knowledge graphs, Optimization, Simulation, Database systems, Decision making, Graph theory, Industrial plants, Industrial research, Knowledge graph, Query processing, Reviews, Virtual corporation, Virtual reality, Group Decision Making, Literature reviews, Manufacturing simulation, Optimisations, Production system, Simulation and optimization, Virtual manufacturing
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences Computer Systems
Research subject
Virtual Production Development (VPD); VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-25767DOI: 10.1109/ACCESS.2025.3596632ISI: 001565196100022Scopus ID: 2-s2.0-105013130528OAI: oai:DiVA.org:his-25767DiVA, id: diva2:1992749
Part of project
Virtual factories with knowledge-driven optimization (VF-KDO), Knowledge Foundation
Funder
Knowledge Foundation, 20180011
Note

CC BY 4.0

Received 27 May 2025, accepted 7 July 2025, date of publication 7 August 2025, date of current version 28 August 2025.

Correspondence Address: R. Senington; University of Skövde, School of Engineering Science, Skövde, 541 28, Sweden; email: richard.james.senington@his.se

This work was supported in part by the Virtual Factories with Knowledge-Driven Optimization (VF-KDO) Research Project under Grant 20180011, and in part by the Knowledge Foundation (KK-Stiftelsen).

Available from: 2025-08-28 Created: 2025-08-28 Last updated: 2025-11-05Bibliographically approved

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Senington, RichardNg, Amos H. C.Mittermeier, LudwigBandaru, Sunith

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