The DOI matches its Crossref record; the references it cites are not checked yet.
Buttressing the power of entity relationships model in database structure and information visualization: Insights from the Technology Association of Georgia’s Digital Health Ecosystem
Open accessAbstract
The state of Georgia, with a population exceeding 10.7 million, is recognized for its robust economy, affordable living standards, and strong commitment to education. Ranked as the 11th fastest-growing state in the United States, Georgia is home to 18 Fortune® 500 and 32 Fortune® 1000 headquarters, housing some of the most innovative healthcare technology companies. As part of its mission to support economic growth and digital transformation, the Technology Association of Georgia (TAG), through its subsidiary, the TAG Digital Health Society, established the TAG Digital Health Ecosystem. This initiative serves as a centralized hub for digital health companies, fostering industry collaboration and innovation. However, the sheer volume of data required to maintain such a platform presents significant challenges in database structuring, data integrity, and information visibility. This study explores how the Entity-Relationship (ER) model can strengthen database structures and optimize information visualization for large-scale digital health platforms. By implementing an ER model, the TAG Digital Health Ecosystem ensures seamless data integration, efficient retrieval, and enhanced user experience for key stakeholders, including potential investors, healthcare providers, digital health companies, job seekers, and researchers. The ER model's ability to define clear relationships among entities enhances the system's navigability, reducing redundancy and improving data accessibility. Moreover, this research highlights best practices in ER modeling, emphasizing its role in improving decision-making, fostering industry partnerships, and supporting Georgia’s growing digital health sector. The findings underscore the importance of structured database frameworks in creating scalable and sustainable digital health infrastructures, ensuring long-term impact and usability.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Although many studies investigate the performance of special database systems, not many synthesise across paradigms in order to draw general trends of performance, integration bottlenecks, and additional performance tradeoffs (Mbanugo et al., 2025).”
From A REVIEW ON OBJECT-ORIENTED, OBJECT-RELATIONAL, RELATIONAL, AND NOSQL DATABASES: EVOLUTION, INTEGRATION, AND PERFORMANCE TRADE-OFFS (2025) · cites this paper as Mbanugo et al., 2025
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 28
- S. R. A. a. D. Batra, "CODASYS: a consulting tool for novice database designers," ACM, vol. 33, no. 3,
- Debbadi RK, Boateng O. Developing intelligent automation workflows in Microsoft Power Automate by embedding deep learning algorithms for real-time process adaptation. Int J Sci Res Arch. 2025;14(2):802-820. doi:10.30574/ijsra.2025.14.2.0449. · DOI
- C. R. Jaimez-González and J. Martínez-Samora, "DiagrammER: A Web Application to Support the Teaching- Learning Process of Database Courses Through the Creation of E-R Diagrams,"International Journal of Emerging Technologies in Learning (iJET), vol. 15, no. 09, 2020.
- PETER PIN-SHAN CHEN (1976): ACM Transactions on Database Systems, Vol. 1, No. 1. March 1976, Pages 9-36.
- Alemade VO. Innovative process technologies: advancing efficiency and sustainability through optimization and control. Int J Res Publ Rev. 2025 Feb;6(2):1941-55. Available from: https://ijrpr.com/uploads/V6ISSUE2/IJRPR38744.pdf.
- Tomich TP, Hoy C, Dimock MR, Hollander AD, Huber PR, Hyder A, Lange MC, Riggle CM, Roberts MT, Quinn JF. Why Do We Need Food Systems Informatics? Introduction to This Special Collection on Smart and Connected Regional Food Systems. Sustainability. 2023 Apr 12;15(8):6556.
- Tremblay MC, Kohli R, Rivero C. Data is the New Protein: How the Commonwealth of Virginia Built Digital Resilience Muscle and Rebounded from Opioid and COVID Shocks. MIS Quarterly. 2023 Mar 1;47(1).
- Narożna D, Adamska J. Innovation Ecosystems and the Role of Communication: Insights from European Science, Technology Parks and Areas of Innovation. Taylor & Francis; 2024 Oct 11.
- Grover V, Niederman F. Research perspectives: The quest for innovation in information systems research: Recognizing, stimulating, and promoting novel and useful knowledge. Journal of the Association for Information Systems. 2021;22(6):1753-82.
- Ajayi, Olumide, Data Privacy and Regulatory Compliance Policy Manual This Policy Manual shall become effective on November 23 rd, 2022 (November 23, 2022). No , Available at SSRN: http://dx.doi.org/10.2139/ssrn.5043087 · DOI
- Rejeb A, Rejeb K, Simske S, Keogh JG. Exploring blockchain research in supply chain management: A latent Dirichlet allocation-driven systematic review. Information. 2023 Oct 12;14(10):557.
- Olumide Ajayi. Data Privacy and Regulatory Compliance: A Call for a Centralized Regulatory Framework. International Journal of Scientific Research and Management (IJSRM). 2024 Dec;12(12):573-584. Available from: https://doi.org/10.18535/ijsrm/v12i12.lla01 · DOI
- Ash J, Kitchin R, Leszczynski A. Digital turn, digital geographies?. Progress in Human Geography. 2018 Feb;42(1):25-43.
- Debbadi RK, Boateng O. Enhancing cognitive automation capabilities with reinforcement learning techniques in robotic process automation using UiPath and Automation Anywhere. Int J Sci Res Arch. 2025;14(2):733-752. doi:10.30574/ijsra.2025.14.2.0450. · DOI
- Ponnusamy VK, Kasinathan P, Madurai Elavarasan R, Ramanathan V, Anandan RK, Subramaniam U, Ghosh A, Hossain E. A comprehensive review on sustainable aspects of big data analytics for the smart grid. Sustainability. 2021 Dec 1;13(23):13322.
- Richardson D. International encyclopedia of geography, 15 volume set: people, the earth, environment and technology. John Wiley & Sons; 2017 Mar 20.
- Chukwunweike JN, Praise A, Bashirat BA, 2024. Harnessing Machine Learning for Cybersecurity: How Convolutional Neural Networks are Revolutionizing Threat Detection and Data Privacy. https://doi.org/10.55248/gengpi.5.0824.2402. · DOI
- Hawkins S, editor. Access and control in digital humanities. Routledge; 2021 May 13.
- Dede C, Richards J, editors. The 60-year curriculum: New models for lifelong learning in the digital economy. Routledge; 2020 Mar 31.
- Rambe P, Bester J. Using historical data to explore transactional data quality of an African power generation company. South African Journal of Information Management. 2020;22(1):1-2.
- Lin N. Applied business analytics: Integrating business process, big data, and advanced analytics. FT Press; 2014 Dec 12.
- Lenzholzer S, Stremke S, Steeneveld GJ, Cortesão J. Urban Climate Phenomena as Renewable Energy Sources: student's design experiments. InEvolving Norms: Adapting Scholarship to Disruptive Phenomena 2022 (pp. 307- 307).
- Tahmasebinia F, Lin L, Wu S, Kang Y, Sepesgozar S. Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct. Buildings. 2024 Jun 12;14(6):1774.
- Kish K, Quilley S. Ecological limits of development: Living with the sustainable development goals. Routledge; 2021 Nov 3.
- Debbadi RK, Boateng O. Optimizing end-to-end business processes by integrating machine learning models with UiPath for predictive analytics and decision automation. Int J Sci Res Arch. 2025;14(2):778-796. doi:10.30574/ijsra.2025.14.2.0448. · DOI
- Nwafor KC, Ikudabo AO, Onyeje CC, Ihenacho DOT. Mitigating cybersecurity risks in financial institutions: The role of AI and data analytics. Int J Sci Res Arch. 2024;13(01):2895-2910. Available from: https://doi.org/10.30574/ijsra.2024.13.1.2014 · DOI
- Hu S, Zhao H, Tang W. Mechanisms of Learning and Innovation in Project Performance: Evidence from Chinese Hydropower Industry. Buildings. 2023 Oct 23;13(10):2665.
- Thorn JP, Klein JA, Steger C, Hopping KA, Capitani C, Tucker CM, Nolin AW, Reid RS, Seidl R, Chitale VS, Marchant R. A systematic review of participatory scenario planning to envision mountain social-ecological systems futures. Ecology and Society. 2020 Jan;25(3).
Cited by
Sorted by citations
Okoronkwo, M. C., Mustapha, M., Markus, C., Adaji, M. A., & Chibunze, N. E. (2025) · Open Journal of Physical Science (ISSN: 2734-2123) · doi:10.52417/ojps.v6i2.1039
Awotunde, O. J. (2025) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.6.0325.1208 · Cited by 7
Khairat, S., Copley, H., Pathak, S., Yalamanchi, K., Angelelli, D., Newman-Matthews, P., & McSwain, D. (2026) · doi:10.2196/preprints.106954
Chen, H., & wang, X. (2026) · International Conference on Machine Intelligence and Digital Applications (MIDA) · doi:10.1145/3801438.3806949
Metadata
- Authors
- Mbanugo, O. J., Taylor, A., & Sneha, S.
- Affiliations
- Kennesaw State University (US)
- Corresponding author
- Olu James Mbanugo
- Venue
- World Journal of Advanced Research and Reviews
- Publisher
- GSC Online Press
- Published
- 2025-02-28
- Volume
- 25
- Issue
- 2
- Pages
- 1294-1313
- ISSN
- 2581-9615 (ISSN-L)
- OpenAlex
- W4407678735
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- World Journal of Advanced Research and Reviews (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Software › Engineering and Information Technology
- Social Sciences › Business, Management and Accounting › Management Information Systems › Decision Support System Applications
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Keywords
- entity-relationship model, database design, information visualization, digital health ecosystem, data integrity, data redundancy, data integration, digital health infrastructure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
