A Data-Driven Assessment Model for Metaverse Maturity
DOI:
https://doi.org/10.15837/ijccc.2024.4.6498Keywords:
Metaverse, Data Driven, Maturity Assessment, K-Means.Abstract
The rapid development of the metaverse has sparked extensive discussion on how to estimate its development maturity using quantifiable indicators, which can offer an assessment framework for governing the metaverse. Currently, the measurable methods for assessing the maturity of the metaverse are still in the early stages. Data-driven approaches, which depend on the collection, analysis, and interpretation of large volumes of data to guide decisions and actions, are becoming more important. This paper proposes a data-driven approach to assess the maturity of the metaverse based on K-means-AdaBoost. This method automatically updates the indicator weights based on the knowledge acquired from the model, thereby significantly enhancing the accuracy of model predictions. Our approach assesses the maturity of metaverse systems through a thorough analysis of metaverse data and provides strategic guidance for their development.
References
Meng, Z.; She, C.; Zhao, G. & Martini, D. (2022). Sampling, Communication, and Prediction Co- Design for Synchronizing the Real-World Device and Digital Model in Metaverse, IEEE Journal on Selected Areas in Communications, 41, 288-300. https://doi.org/10.1109/JSAC.2022.3221993
Star X.Zhao; Qiao Lili; Fred Y.Ye.(2022). A Review of Metaverse Research and Applications, Journal of Information Resources Management, 2022, 12(4): 12-23,45.
Wang, Y.; Su Z.; Zhang N. et al.(2022). A Survey on Metaverse: Fundamentals, Security, and Privacy, IEEE Communications Surveys & Tutorials, 2022, 25: 319-52. https://doi.org/10.1109/COMST.2022.3202047
Lee, L-H; Braud, T.; Zhou, P. et al. (2021). All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda, ArXiv, 2021, abs/2110.05352.
Wang, H.; Ning, H.; Lin, Y. et al. (2023). A Survey on the Metaverse: The State-of-the-Art, Technologies, Applications, and Challenges, IEEE Internet of Things Journal, 2023, 10: 14671- 88. https://doi.org/10.1109/JIOT.2023.3278329
Dionisio, J. D. N.; III, W. G. B., Gilbert, R. (2013). 3D Virtual worlds and the metaverse: Current status and future possibilities, ACM Comput Surv, 2013, 45(3): Article 34. https://doi.org/10.1145/2480741.2480751
Park, S-M; Kim, Y-G. (2022). A Metaverse: Taxonomy, Components, Applications, and Open Challenges, IEEE Access, 2022, 10: 4209-51. https://doi.org/10.1109/ACCESS.2021.3140175
Zainab, H.E.; Bawany, N.Z.; Imran, J. & Rehman, W. (2022). Virtual Dimension-A Primer to Metaverse, IT Professional, 24, 27-33. https://doi.org/10.1109/MITP.2022.3203820
Weinberger, M.; Gross, D. (2023). A Metaverse Maturity Model, Global Journal of Computer Science and Technology, 2023. https://doi.org/10.34257/GJCSTHVOL22IS2PG39
Pamučar, D.; Deveci, M.; Gokasar, I. et al. (2022). A metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms, Technological Forecasting and Social Change, 2022. https://doi.org/10.1016/j.techfore.2022.121778
Jin, Y.; Zhang, H.; Zhao, L. et al. (2023). Data driven improvement of user perception in mobile business halls, Communication World, 2023, (20): 40-1.
Song, Y.N.; Li, Z.; Wang, Y. et al. (2023). Research on data-driven spatial situational entity cognition methods, The 11th China Command and Control Conference, Beijing, China, F, 2023 [C].
Li, Y.P. (2023). Data driven decision-making: key factors in smart library management, Culture Monthly, 2023, (09): 120-2.
Wu, H.; Liu, J.H.; Zhu, H. et al. (2023). Data driven traffic signal control perception evaluation diagnosis optimization closed-loop technology (II): State perception, Road Traffic Management, 2023, (09): 28-31.
Yu, J. E. (2022). Exploration of educational possibilities by four metaverse types in physical education, Technologies, 10(5), 104. https://doi.org/10.3390/technologies10050104
VİSCONTİ, R. M. (2022). From physical reality to the Metaverse: a Multilayer Network Valuation, Journal of Metaverse, 2(1), 16-22.
Eom, H.; Kim, K.; Lee, S. et al. (2019). Development of virtual reality continuous performance test utilizing social cues for children and adolescents with attention-deficit/hyperactivity disorder, Cyberpsychology, Behavior, and Social Networking, 2019, 22(3): 198-204. https://doi.org/10.1089/cyber.2018.0377
SOLOMATINE, D. P.; SEE, L. M.; ABRAHART, R. J. (2017). Chapter 2 Data-Driven Modelling, Concepts , Approaches and Experiences, F, 2017 [C].
Park, S. M. & Kim, Y. G. (2022). A Metaverse: Taxonomy, Components, Applications, and Open Challenges, IEEE Access, 10, 4209-4251. https://doi.org/10.1109/ACCESS.2021.3140175
DAMAR, M. (2021). Metaverse Shape of Your Life for Future: A bibliometric snapshot, Journal of Metaverse, 1(1), 1-8.
Njoku, J. N.; Nwakanma, C. I.; Amaizu, G. C. & Kim, D. S. (2023). Prospects and challenges of Metaverse application in data-driven intelligent transportation systems, IET Intelligent Transport Systems, 17(1), 1-21. https://doi.org/10.1049/itr2.12252
Guston, D. H. & Sarewitz, D. (2020). Real-time technology assessment, In Emerging Technologies, (pp. 231-247). Routledge. https://doi.org/10.4324/9781003074960-21
Zhang, D. (2017). High-speed train control system big data analysis based on the fuzzy RDF model and uncertain reasoning, International Journal of Computers Communications & Control, 12(4), 577-591. https://doi.org/10.15837/ijccc.2017.4.2914
Zhang, D.; Sui, J. & Gong, Y. (2017). Large scale software test data generation based on collective constraint and weighted combination method, Tehnicki Vjesnik/Technical Gazette, 24(4). https://doi.org/10.17559/TV-20170319045945
Chen, W. (2023). Deep adversarial neural network model based on information fusion for music sentiment analysis, Computer Science and Information Systems, (00), 31-31.
Dzitac, I.; Filip, F. G. & Manolescu, M. J. (2017). Fuzzy logic is not fuzzy: World-renowned computer scientist Lotfi A. Zadeh, International Journal of Computers Communications & Control, 12(6), 748-789. https://doi.org/10.15837/ijccc.2017.6.3111
Filip, F. G. (2022). Collaborative decision-making: concepts and supporting information and communication technology tools and systems, International Journal of Computers Communications & Control, 17(2). https://doi.org/10.15837/ijccc.2022.2.4732
Constantinescu, Z.; Marinoiu, C. & Vladoiu, M. (2010). Driving style analysis using data mining techniques, International Journal of Computers Communications & Control, 5(5), 654-663. https://doi.org/10.15837/ijccc.2010.5.2221
Lyu, Z. (2023). State-of-the-art human-computer-interaction in metaverse, International Journal of Human-Computer Interaction, 1-19. https://doi.org/10.1080/10447318.2023.2248833
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 Mincong TANG, Jie Cao, Dalin Zhang
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
