Database Management Systems (2000–2026): A Scopus-Based Bibliometric Review of Research Streams and Key Contributor
DOI:
https://doi.org/10.58812/wsist.v4i01.2832Keywords:
Database Management Systems (DBMS), Bibliometric Analysis, Scopus, VOSviewer, Key ContributorAbstract
The current research intends to analyze the knowledge structure and research trends of the DBMS by using a bibliometric approach to study articles published in the Scopus database between 2000 and 2026. This will be achieved using the VOSviewer as the main software for analyzing authors' networks, citations and keywords' co-occurrences. As expected, the findings reveal that research within the DBMS area has become increasingly interdisciplinary with notable influences from fields such as health care and bioinformatics and data science. In terms of authorship patterns, it is evident that collaboration within DBMS research happens in clusters. This is an indication of the presence of well-coordinated intra-cluster collaboration and a lack of inter-cluster collaboration. The findings from the citation analysis show that there are key publications within DBMS research that have been highly cited due to their contribution to developing the discipline. Finally, from the keyword co-occurrences, it is clear that the trend within DBMS research has shifted from more traditional subjects to newer areas such as artificial intelligence and machine learning. The findings highlight the growing complexity and integration of DBMS within modern digital ecosystems, while also emphasizing the need for enhanced global collaboration and innovation-driven research. This study provides valuable insights for researchers and practitioners in understanding the trajectory and future directions of DBMS research.
References
[1] T.-T. T. Phan, C.-T. Vu, P.-T. T. Doan, D.-H. Luong, and T.-P. Bui, “Two decades of studies on learning management system in higher education: A bibliometric analysis with Scopus database 2000-2020,” J. Univ. Teach. Learn. Pract., vol. 19, no. 3, pp. 1–21, 2022.
[2] R. Farooq, “A review of knowledge management research in the past three decades: a bibliometric analysis,” Vine J. Inf. Knowl. Manag. Syst., vol. 54, no. 2, pp. 339–378, 2024.
[3] P. T. Chountalas and A. G. Lagodimos, “Integrated management systems: a content and bibliometric analysis,” TQM J., vol. 37, no. 7, pp. 1827–1873, 2025.
[4] Y. Gu, “Global knowledge management research: A bibliometric analysis,” Scientometrics, vol. 61, no. 2, pp. 171–190, 2004.
[5] P.-T. Pham et al., “Learning management system in developing countries: A bibliometric analysis between 2005 and 2020,” Eur. J. Educ. Res., vol. 11, no. 3, pp. 1363–1377, 2022.
[6] H. A. Schildt, “Sitkis: software for bibliometric data management and analysis,” Helsinki Inst. Strateg. Int. Bus., vol. 6, no. 1, 2002.
[7] Q. Wang, G. M. Garrity, J. M. Tiedje, and J. R. Cole, “Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy,” Appl. Environ. Microbiol., vol. 73, no. 16, pp. 5261–5267, 2007.
[8] S. Rozen and H. Skaletsky, “Primer3 on the WWW for general users and for biologist programmers,” in Bioinformatics methods and protocols, Springer, 2000, pp. 365–386.
[9] E. Cerami et al., “The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data,” Cancer Discov., vol. 2, no. 5, pp. 401–404, 2012.
[10] M. D. Wilkinson et al., “Comment: The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3.” 2016.
[11] B. R. Haugen et al., “2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer,” thyroid, vol. 26, no. 1, pp. 1–133, 2016.
[12] A. Conesa, S. Götz, J. M. García-Gómez, J. Terol, M. Talón, and M. Robles, “Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research,” Bioinformatics, vol. 21, no. 18, pp. 3674–3676, 2005.
[13] S. Michie, M. M. Van Stralen, and R. West, “The behaviour change wheel: a new method for characterising and designing behaviour change interventions,” Implement. Sci., vol. 6, no. 1, p. 42, 2011.
[14] F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Trans. neural networks, vol. 20, no. 1, pp. 61–80, 2008.
[15] K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee, “A design science research methodology for information systems research,” J. Manag. Inf. Syst., vol. 24, no. 3, pp. 45–77, 2007.
[16] K. Barnett, S. W. Mercer, M. Norbury, G. Watt, S. Wyke, and B. Guthrie, “Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study,” Lancet, vol. 380, no. 9836, pp. 37–43, 2012.
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