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Czasopismo
2021 | 17 | nr 3 | 321--329
Tytuł artykułu

Hypotheses Concerning Complexity Surges in Modern and Future Industrial Information Systems

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Background: This paper has the central aim to provide an analysis of increases of system complexity in the context of modern industrial information systems. An investigation and exploration of relevant theoretical frameworks is conducted and accumulates in the proposition of a set of hypotheses as an explanatory approach for a possible definition of system complexity based on information growth in industrial information systems. Several interconnected sources of technological information are investigated and explored in the given context in their functionality as information transferring agents, and their practical relevance is underlined by the application of the concepts of Big Data and cyber-physical, cyber-human and cyber-physical-cyber-human systems. Methods: A systematic review of relevant literature was conducted for this paper and in total 85 sources matching the scope of this article, in the form of academic journals and academic books of the mentioned academic fields, published between 2012 and 2019, were selected, individually read and reviewed by the authors and reduced by careful author selection to 17 key sources which served as the basis for theory synthesis. Results: Four hypotheses (H1-H4) concerning exponential surges of system complexity in industrial information systems are introduced. Furthermore, first foundational ideas for a possible approach to potentially describe, model and simulate complex industrial information systems based on network, agent-based approaches and the concept of Shannon entropy are introduced. Conclusion: Based on the introduced hypotheses it can be theoretically indicated that the amount information aggregated and transferred in a system can serve as an indicator for the development of system complexity and as a possible explanatory concept for the exponential surges of system complexity in industrial information systems. (original abstract)
Słowa kluczowe
Czasopismo
Rocznik
Tom
17
Numer
Strony
321--329
Opis fizyczny
Twórcy
autor
  • University of Lincoln, United Kingdom
  • University of Lincoln, United Kingdom
Bibliografia
  • Duan et. al., 2019. Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda, International Journal for Information Management, 48, http://doi.org/10.1016/j.ijinfomgt.2019.01.021
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  • Gaham M., Bouzouia B., Achour N., 2015. Human-in-the-Loop Cyber-Physical Production Systems Control (HiLCP2sC): A Multi-objective Interactive Framework Proposal. In: Borangiu T., Thomas A., Trentesaux D. (eds) Service Orientation in Holonic and Multi-agent Manufacturing. Studies in Computational Intelligence, 594. Springer, Cham. http://doi.org/10.1007/978-3-319-15159-5_29
  • Garcia et. al, 2019. A human-in-the-loop cyber-physical system for collaborative assembly in smart manufacturing, Procedia CIRP, 81, 600-605, http://doi.org/10.1016/j.procir.2019.03.162
  • Gimpel H., Röglinger M., 2015. Digital transformation: changes and chances, Project Group Business and Information Systems Engineering (BISE) of the Fraunhofer Institute for Applied Information Technology FIT, https://eref.unibayreuth.de/id/eprint/29908
  • Hanel et. al., 2014. How multiplicity determines entropy and the derivation of the maximum entropy principle for complex systems, Proc Natl Acad Sci U S A., 111, 19, 6905-6910, http://doi.org/10.1073/pnas.1406071111
  • Horvarth I. Gerritsen B., 2012. Cyber physical systems: concepts, technologies and implementations, Proceedings of TMCE 2012, May 7-11, 2012, Karlsruhe, Germany, ISBN 978-90-5155-082-5.
  • Jalili M., Perc M., 2017. Information cascades in complex networks, Journal of Complex Networks, 5, 665-693, http://doi.org/10.1093/comnet/cnx019
  • Li H., 2016. Information Efficiency of Communications for Networked Control in Cyber-Physical Systems: When Carnot Meets Shannon, 2016 IEEE 55th Conference on Decision and Control (CDC), 2016, 12-14, http://doi.org/10.1109/CDC.2016.7798536
  • McAfee A., Brynjolfsson E., 2014. The second machine age, W.W. Norton & Company.
  • Meijer D., 2013. Information: What do you mean?, Syntropy Journal Syntropy 2013 (3): 1-49 ISSN 1825-796.
  • Mourtzis et. al., 2019. Modelling and quantification of industry 4.0 manufacturing complexity based on information theory: a robotics case study, International Journal of Production Research, 57,22, 2019. http://doi.org/10.1080/00207543.2019.1571686
  • Nagy et. al., 2012. Statistical Basis for Predicting Technological Progress, Statistical Basis for Predicting Technological Progress. PLoS ONE, 8, 2, http://doi.org/10.1371/journal.pone.0052669
  • Riahi et.al., 2018. Big Data and Big Data Analytics: Concepts, Types and Technologies, International Journal of Research and Engineering, 5, 9, http://doi.org/10.21276/ijre.2018.5.9.5
  • Spencer D., 2016. Work in and beyond the Second Machine Age: the politics of production and digital technologies, Work, employment and society, 31, http://doi.org/10.1177/0950017016645716
  • Terrazas et. al., 2015. Complexity measurement based on information theory and Kolmogorov complexity, Artificial Life, 21, 2, 1-20.
  • Törngren M. Sellgren U., 2018. Complexity Challenges in Development of Cyber-Physical, Systems, Springer International Publishing AG, part of Springer Nature 2018, 2018,478-503, http://doi.org/10.1007/978-3-319-95246-8_27
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171624192

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