A Review of Integrated Artificial Intelligence and Big Data Analytics Models for Intelligent Decision-Making

Zice Gao

doi:10.71222/qdmgr066

Authors

Zice Gao University of Rochester, Rochester, NY 14627, USA Author

DOI:

https://doi.org/10.71222/qdmgr066

Keywords:

Artificial Intelligence, Big Data Analytics, Intelligent Decision-Making, Integrated Models, Future Perspectives

Abstract

This review paper examines the integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) models for intelligent decision-making. The study explores historical advancements, core methodologies, comparative analyses, challenges, and future perspectives. By dissecting the synergy between AI and BDA, the paper highlights their combined potential in optimizing decision-making processes across industries. Key themes include the evolution of AI-BDA frameworks, algorithmic innovations, and real-world applications. The paper also delves into challenges such as scalability, data privacy, and ethical considerations, while proposing future directions for research and development. The findings aim to provide a comprehensive understanding of the current state and future trajectory of integrated AI-BDA models.

References

1. V. S. Magomadov, "The application of artificial intelligence and Big Data analytics in personalized learning," J. Phys.: Conf. Ser., vol. 1691, no. 1, p. 012169, Nov. 2020.

2. A. M. Rahmani et al., "Artificial intelligence approaches and mechanisms for big data analytics: a systematic study," PeerJ Comput. Sci., vol. 7, p. e488, 2021.

3. A. Deshpande and M. Kumar, Artificial Intelligence for Big Data: Complete Guide to Automating Big Data Solutions Using Artificial Intelligence Techniques. Birmingham: Packt Publishing Ltd., 2018.

4. S. Almanasra, "Applications of integrating artificial intelligence and big data: A comprehensive analysis," J. Intell. Syst., vol. 33, no. 1, p. 20240237, 2024.

5. P. V. Thayyib et al., "State-of-the-art of artificial intelligence and big data analytics reviews in five different domains: a bibliometric summary," Sustainability, vol. 15, no. 5, p. 4026, 2023.

6. B. C. C. Marella et al., "A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big Data Analytics," in 2025 First Int. Conf. Adv. Comput. Sci., Electr., Electron., Commun. Technol. (CE2CT), pp. 1139-1144, Feb. 2025.

7. V. Winata, "Optimizing Big Data Processing Through Artificial Intelligence: A Systematic Literature Review," Aira (Artif. Intell. Res. Appl. Learn.), vol. 1, no. 2, pp. 1-9, 2022.

8. A. H. Gandomi, F. Chen, and L. Abualigah, "Big data analytics using artificial intelligence," Electronics, vol. 12, no. 4, p. 957, 2023.

9. A. M. Sandoval and T. Redondo, "Text analytics: the convergence of big data and artificial intelligence," IJIMAI, vol. 3, no. 6, pp. 57-64, 2016.

10. R. Remala et al., "Enhancing Big Data Analytics With Artificial Intelligence: Innovative Techniques And Applications In Various Sectors," in 2024 Int. Conf. Intell. Comput. Emerg. Commun. Technol. (ICEC), pp. 1-6, Nov. 2024.

11. K. Kalinaki, U. Yahya, O. A. Malik, and D. T. C. Lai, "A review of big data analytics and Artificial Intelligence in Industry 5.0 for smart decision-making," Human-centered approaches in industry 5.0: Human-machine interaction, virtual reality training, and customer sentiment analysis, pp. 24-47, 2024.

12. M. A. Hamza et al., "Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework," Comput. Mater. Continua, vol. 73, no. 2, 2022.