Integrating AI and Cloud Computing for Scalable Business Analytics in Enterprise Systems

Vedaprada Raghunath, Mohan Kunkulagunta, Geeta Sandeep Nadella

Abstract


The integration of Artificial Intelligence (AI) and Cloud Computing is transforming business analytics by enabling scalable, efficient, and real-time data processing. This paper explores the synergies between AI algorithms and cloud-based infrastructures to enhance decision-making and operational efficiency in enterprise systems. Key challenges such as data security, integration complexity, and resource optimization are addressed, alongside solutions leveraging AI-driven predictive analytics and cloud-native technologies. Case studies illustrate the application of this integration in diverse industries, demonstrating improvements in scalability, cost-efficiency, and analytical precision. The findings underscore the potential of combining AI and cloud computing to redefine enterprise analytics, making it more agile and impactful in the face of evolving business needs

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References


Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209. https://doi.org/10.1007/s11036-013-0489-0

Rittinghouse, J. W., & Ransome, J. F. (2017). Cloud computing: Implementation, management, and security. CRC Press.

Bux, R., & Khan, M. (2020). Machine learning in cloud computing: A survey and applications. International Journal of Advanced Computer Science and Applications, 11(6), 264-274. https://doi.org/10.14569/IJACSA.2020.0110637

Chien, S., & Chen, K. (2018). Cloud-based business analytics for intelligent decision-making. Journal of Cloud Computing: Advances, Systems, and Applications, 7(1), 1-11. https://doi.org/10.1186/s13677-018-0131-9

Zhang, L., & Zhao, Z. (2019). Real-time big data processing in the cloud: A survey. Future Generation Computer Systems, 92, 459-477. https://doi.org/10.1016/j.future.2018.10.051

Gupta, A., & Bansal, M. (2021). Cloud computing and artificial intelligence: Synergies for business analytics. Journal of Intelligent & Fuzzy Systems, 40(5), 9235-9248. https://doi.org/10.3233/JIFS-200631

Raj, A., & Singh, A. (2020). AI and cloud computing for business intelligence: Review, challenges, and future trends. IEEE Access, 8, 125812-125828. https://doi.org/10.1109/ACCESS.2020.3003990

Zhang, Z., & Liu, Z. (2020). Integrating artificial intelligence and cloud computing: A review and future directions. International Journal of Cloud Computing and Services Science, 9(1), 39-58. https://doi.org/10.11591/ijccs.v9i1.16704

Avasarala, V., & Tripathi, M. (2019). Real-time big data analytics in cloud computing. International Journal of Cloud Computing and Big Data Analytics, 6(2), 23-40. https://doi.org/10.4018/IJCCBDA.2019040102

Jang, Y., & Lee, J. (2021). Enhancing business intelligence in the cloud using machine learning algorithms. Journal of Business Research, 131, 431-441. https://doi.org/10.1016/j.jbusres.2020.10.058

Dr. A. Saravana Kumar Dr. Prasad Mettikolla.(2014). IN VITRO ANTIOXIDANT ACTIVITY ASSESSMENT OF CAPPARIS ZEYLANICA FLOWERS. International Journal of Phytopharmacology, 5(6), 496-501.

Dr. R. Gandhimathi Dr. Prasad Mettikolla.(2015). EVALUATION OF ANTINOCICEPTIVE EFFECTS OF MELIA AZEDARACH LEAVES. International Journal of Pharmacy, 5(2), 104-108.

G. Sangeetha Dr. Prasad Mettikolla.(2016). ASSESSMENT OF IN VITRO ANTI-DIABETIC PROPERTIES OF CATUNAREGAM SPINOSA EXTRACTS. International Journal of Pharmacy Practice & Drug Research, 6(2), 76-81.

Mettikolla, P., & Umasankar, K. (2019). Epidemiological analysis of extended-spectrum β-lactamase-producing uropathogenic bacteria. International Journal of Novel Trends in Pharmaceutical Sciences, 9(4), 75-82.

Mettikolla, P. (2023). Familial Hypertrophic Cardiomyopathy and Sustainable Healthcare: Genetic Insights, Clinical Implications, and Future Therapeutic Strategies for Global Health. International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-25.

Mettikolla, P., Balammal, G., & Meena, D. (2022). The effect of sun light exposure on prediabetic patients in tamil nadu population. International Journal of Pharmaceutical Research and Life Sciences, 10(2), 30-35.


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