Open Access
An Advanced Analytical Architecture for Leveraging Big Data in Artificial Intelligence Systems: Techniques, Optimization Strategies, and Case-Based Evaluation
4
Faculty of Information Technology Vietnam Institute of Intelligent Computing Hanoi, Vietnam
4
Department of Machine Learning and Analytics Ho Chi Minh Digital Technology University Ho Chi Minh City, Vietnam
Abstract
The rapid evolution of Artificial Intelligence (AI) systems has been significantly accelerated by the proliferation of Big Data, enabling more accurate, scalable, and adaptive computational models. However, integrating Big Data with AI systems introduces critical challenges related to data heterogeneity, computational scalability, privacy constraints, and system optimization. This paper proposes an advanced analytical architecture designed to efficiently leverage Big Data for AI-driven systems through structured data pipelines, optimization strategies, and case-based evaluation frameworks. The study synthesizes foundational theories in machine learning, distributed computing, and data analytics while critically examining architectural models such as MapReduce-based frameworks, edge-cloud hybrid systems, and machine learning pipelines. Drawing upon established literature, including healthcare analytics, Industry 4.0 systems, and privacy-preserving data models, the proposed framework integrates multi-layered data processing, adaptive optimization mechanisms, and AI model orchestration techniques. The research also highlights the socio-technical implications of Big Data systems, emphasizing ethical concerns, interpretability, and scalability constraints as emphasized in prior scholarly discourse (Boyd and Crawford, 2012). Findings suggest that a unified analytical architecture improves computational efficiency, reduces latency in decision-making, and enhances predictive accuracy across domains such as healthcare, smart systems, and financial analytics. The paper contributes a structured conceptual model for researchers and practitioners aiming to design next-generation AI systems powered by Big Data ecosystems.
Keywords
Big Data Analytics,
Artificial Intelligence,
Analytical Architecture,
Machine Learning Systems
References
Abdellatif, A.A., Mohamed, M.A., Chiasserini, C.F. and Erbad, A. (2019). Edge Computing for Smart Health:Context-Aware Approaches, Opportunities, and Challenges. IEEE Network, 33(3), 196-203.
Aggarwal, C.C. (2015). Data Mining: The Textbook. Springer.
Alpaydin, E. (2020). Introduction to Machine Learning, 4th Edition, MIT Press.
Boyd, D. and Crawford, K. (2012). Critical Questions for Big Data: Provocations for a Cultural, Technological,and Scholarly Phenomenon. Information, Communication & Society, 15(5), 662-679.
Chen, C., Zhang, B. and He, X. (2015). A Survey on Big Data Analytics in Healthcare. Journal of Biomedical andHealth Informatics, 19(4), 1193-1209.
Chen, H., Chiang, R.H.L. and Storey, V.C. (2012). Business Intelligence and Analytics: From Big Data to BigImpact. MIS Quarterly, 36(4), 1165-1188.
Dakup, P.P. et al. (2023). Targeted Quantification of Protein Phosphorylation and its Contributions towardsMathematical Modeling of Signaling Pathways. Molecules, 28(3), 1143.
Dean J. and Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1).
Emmert-Streib, F. (2020). From the Digital Data Revolution to Digital Health and Digital Economy Toward aDigital Society: Pervasiveness of Artificial Intelligence. arXiv preprint arXiv, 2008.12672, August.
Gandomi, A. and Haider, M. (2015). Beyond the Hype: Big Data Concepts, Methods, and Analytics. InternationalJournal of Information Management, 35(2), 137-144.
Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning. MIT Press.
Hajkowicz, S., Sanderson, C., Karimi, S., Bratanova, A. and Naughtin, C. (2023). Artificial Intelligence Adoptionin the Physical Sciences, Natural Sciences, Life Sciences, Social Sciences and the Arts and Humanities: ABibliometric Analysis of Research Publications from 1960-2021. arXiv preprint arXiv:2306.09145, June.
Himeur, Y. et al. (2023). AI-Big Data Analytics for Building Automation and Management Systems: A Survey,Actual Challenges and Future Perspectives. Artificial Intelligence Review, 56(6), 4929-5021.
Hiniduma, K., Byna, S. and Bez, J.L. (2024). Data Readiness for AI: A 360-Degree Survey. arXiv preprintarXiv:2404.05779, April.
Hinton, G. (2018). Deep Learning—A Technology with the Potential to Transform Health Care. JAMA, 320(11),1101-1102.
Jagatheesaperumal, H., Rahouti, M., Ahmad, K., Al-Fuqaha, A. and Guizani, M. (2021). The Duo of ArtificialIntelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and FutureResearch Directions. IEEE Access, 9, 127527-127552.
Kua, J., Loke, S.W., Arora, C., Fernando, N. and Ranaweera, C. (2021). Internet of Things in Space: A ReviewofOpportunities and Challenges from Satellite-Aided Computing to Digitally-Enhanced Space Living. Sensors,21(23), 8117.
L’Heureux, A., Grolinger, K., Elyamany, H.F. and Capretz, M.A.M. (2017). Machine Learning with Big Data:Challenges and Approaches. IEEE Access, 5, 7776-7797.
Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group ResearchNote, February.
Lynch, C. (2008). Big Data: How Do Your Data Grow?. Nature, 455(7209), 28-29.
Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to DeliverExtraordinary Results. Wiley.
Mehta, N., Pandit, A. and Shukla, S. (2019). Transforming Healthcare with Big Data Analytics and ArtificialIntelligence: A Systematic Mapping Study. Journal of Biomedical Informatics, 100, 103311.
Obschonka, M. and Audretsch, D.B. (2019). Artificial Intelligence and Big Data in Entrepreneurship: A NewEra has Begun. arXiv preprint arXiv:1906.00553, June.
Palermo, C., Leith, E., Cutrona, L. and Porcello, L. (1960). Optical Data Processing and Filtering Systems. IRETransactions on Information Theory, 6(3), 386-400.
Rahmani, M., Babanejad, R., and Mirian, M.S. (2021). Artificial Intelligence Approaches and Mechanisms forBig Data Analytics: A Systematic Study. Journal of Big Data, 8(1), 1-25.
Rubel, M.T.H., Emran, A.K.M., Borna, R.S., Saha, R. and Hasan, M. (2024). AI-Driven Big Data Transformationand Personally Identifiable Information Security in Financial Data: A Systematic Review. Journal of MachineLearning, Data Engineering and Data Science, 1(01), 114-128.
Sarma, A.A.D., Jain, A. and Machanavajjhala, A. (2014). Privacy-Preserving Data Analytics: The Next Frontier.IEEE Data Eng. Bull., 37(4), 52-58.
Senthil, R., Anand, T., Somala, C.S. and Saravanan, K.M. (2024). Bibliometric Analysis of Artificial Intelligencein Healthcare Research: Trends and Future Directions. Future Healthcare Journal, 11(3), 100182.
Sultana, R. (2024). Artificial Intelligence for Decision Making in the Era of Big Data Evolution. Journal ofBusiness Intelligence and Management Information Systems Research, 1(01), 17-40.
Wang, L. and Alexander, C.A. (2020). Big Data Analytics in Medical Engineering and Healthcare: Methods,Advances and Challenges. Journal of Medical Engineering & Technology, 44(6), 267-283.
Similar Articles
- Dr. Arman V. Solberg, Prof. Elina K. Marovic, Machine Learning Approaches for Detecting Interventions and Conditions to Elevate Power Utilization in Established Facilities , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Javier M. Ortega, Dr. Lucia Fernández-Ríos, Predictive Modeling of Online Retail Revenue Using Data Exploration and Intelligent Algorithms , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Mateo Laurent Dufour, Architecting Secure and Scalable Production Machine Learning Systems: Integrating Model Management, High Performance Computing, and Cloud Native Infrastructure , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Lucas Vermeulen, Sophie De Smet, Dr. Thomas Dubois, Integrated Temporal Analytics and AI-Based Approaches for Predicting Culinary Ingredient Consumption Patterns: Evidence from Thai Markets , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Kwame Mensah, Dr. Abena Owusu, An Interpretable Visual Analytics Framework for Machine Learning–Based Multichannel Time Series Classification and Performance Evaluation , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Tashi Wangchuk, Karma Lhendup, Data-Driven Model Supporting Defect Analysis through Vision Techniques in Press-Formed Vehicle Components , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Alexei V. Morozov, Dr. Elena S. Petrova, Identification of Harmful Programs Using a Fusion of Deep Feature Extraction Networks and Context-Aware Sequential Modeling Techniques , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Igor Litovsky, A Systematic Review of Machine Learning Approaches For AI-Driven Fraud Detection in Loyalty Programs , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Oliver Henry Mitchell, A Comprehensive Framework for Intelligent Data Analytics in Modern Intelligent Systems: Design, Methods, and Applications , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- Dr Adrian Morrow, Dynamic AI Based Credit Scoring and Alternative Data Driven Risk Governance in Digital Lending Platforms , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 11 (2025): Volume 02 Issue 11
1-10 of 60
Next
You may also start an advanced similarity search for this article.