Leveraging AI and Machine Learning for Enhanced Data Analytics and Visualization in Database Management With Digital Twins
The convergence of digital twin technology and data analytics continues in the area of smart cities focusing on the comprehensive study of data analysis and its visualization. It begins by standing a foundational framework for data analytics and discussing the importance of these ideas in figuring out complex patterns, concluding, and supporting thoughtful decision-making. The article emphasizes the crucial role of data analytics for urban innovation in the context of smart cities using digital twins. The study delves into the complexities of data collection, integration challenges, and innovative solutions, underscoring the necessity of constructing a robust digital twin ecosystem with a variety of sensors and data sources with its visualization. Smart recommendations by monitoring, prescriptive, and real-time analytics are becoming essential tools for vigilant urban management for taking the best and next course of action. The article delves into predictive analytics, highlighting the synergy of data streams for a comprehensive understanding of urban dynamics.
Authors
- Razaz Waheeb Attar (ORCID: https://orcid.org/0000-0001-9819-7945)
- Sunil K. Singh (ORCID: https://orcid.org/0000-0003-4876-7190)
- Sudhakar Kumar (ORCID: https://orcid.org/0000-0001-7928-4234)
- Brij B. Gupta (ORCID: https://orcid.org/0000-0003-4929-4698)
- Kwok Tai Chui (ORCID: https://orcid.org/0000-0001-7992-9901)
- Varsha Arya (ORCID: https://orcid.org/0000-0001-7549-4429)
- Shivam Goyal (ORCID: https://orcid.org/0009-0009-9374-7907)
- Ahmed Alhomoud (ORCID: https://orcid.org/0000-0003-2782-3106)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Northern Border University (SA)
- City University of Hong Kong (HK)
- China Medical University (TW)
- China Medical University Hospital (TW)
- University of Business and Technology (SA)
- Panjab University (IN)
- University of Petroleum and Energy Studies (IN)
Publication Details
- Journal
- Journal of Database Management
- Published
- 2025-09-09
- DOI
- https://doi.org/10.4018/jdm.388135
- Primary Topic
- Economic and Technological Systems Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Princess Nourah Bint Abdulrahman University