Scalable Cloud Based Information Systems for Intelligent Business Operations and Analytics with Secure Data Processing

Anne Koziolek; Anne Koziolek

doi:10.15662/IJSRAT.2023.0604010

Scalable Cloud Based Information Systems for Intelligent Business Operations and Analytics with Secure Data Processing

Anne Koziolek

Independent Researcher, France

Abstract

Cloud computing has revolutionized enterprise information systems by enabling organizations to process, manage, and analyze massive volumes of business data with improved scalability, flexibility, and operational efficiency. The integration of intelligent analytics, artificial intelligence, machine learning, and secure data processing has further transformed cloud-based business operations into adaptive and data-driven ecosystems capable of supporting strategic decision-making. This study proposes a scalable cloud-based information system designed to enhance intelligent business operations while ensuring secure data processing across distributed computing environments. The proposed framework integrates cloud infrastructure, big data analytics, AI-driven predictive models, Internet of Things (IoT) connectivity, and advanced cybersecurity mechanisms including encryption, identity management, authentication, and blockchain-assisted data integrity verification. The research methodology adopts a systematic design science approach supported by simulation-based implementation and performance evaluation using scalability, response time, throughput, resource utilization, latency, security resilience, and analytical accuracy as performance indicators. The framework demonstrates significant improvements in processing efficiency, workload balancing, predictive business intelligence, and secure information exchange compared with conventional enterprise architectures. The proposed scalable architecture supports digital transformation initiatives across healthcare, manufacturing, finance, retail, logistics, education, and smart city environments. The study concludes that intelligent cloud-based information systems provide sustainable, secure, and highly scalable solutions for modern enterprises operating within increasingly complex and data-intensive digital ecosystems

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2023.0604010
Pages	10364-10373
Published	July 15, 2023
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Anne Koziolek (%2023). Scalable Cloud Based Information Systems for Intelligent Business Operations and Analytics with Secure Data Processing. International Journal of Science, Research and Technology , Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 10364-10373. https://doi.org/10.15662/IJSRAT.2023.0604010

References

1. Buyya, R., Broberg, J., & Goscinski, A. (Eds.). (2011). Cloud Computing: Principles and Paradigms. Wiley.
2. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. https://doi.org/10.1145/1721654.1721672
3. Meesala, A. (2022). Adaptive Spread Anomaly Intelligence Framework (ASAIF): A cloud-native AI framework for real-time bid-ask spread anomaly detection and cross-venue liquidity risk intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9597–9604.
4. Mohammed, S. (2022). Designing secure zero trust architectures for global enterprise environments. International Journal of Science, Research and Technology (IJSRAT), 5(6), 8953–8956.
5. Joyce, S. (2022). Redefining Resilience Through Architectural Innovation and Operational Excellence in SAP HANA Backup Implementation on Microsoft Azure for Scalable Secure and Intelligent Data Protection. Journal Code, 1347, 8520.
6. Devineni, A. (2022). Proactive incident detection in multi-tenant financial cloud platforms. International Journal of Science, Research and Technology (IJSRAT), 5(4), 8136–8139.
7. Konakalla, K. (2024). Building an end-to-end hiring process in Salesforce: Automating recruitment with custom objects, approval processes, and Lightning components. International Journal of Scientific Research in Engineering and Management, 8, 1-6.
8. Gururaj Veershetty. (2022). Digital modernization of gas utility operations: Architecture, scaled-agile delivery, and assurance. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7791–7796.
9. Gopisetty, S. (2022). Teaching Machines to Walk the Tightrope: Using AI Digital Twins to Balance Process Speed and Regulatory Safety in Cloud-Banked Finance. Journal of Scientific and Engineering Research, 9(8), 183-226.
10. Polamreddy, V. R. (2021). Engineering Reversible Enterprise Data Migrations: A Phased Rollout Framework for Financially Critical Retail Platforms. International Journal of Computer Technology and Electronics Communication, 4(2), 3414-3427.
11. Manda, P. (2022). Implementing hybrid cloud architectures with Oracle and AWS: Lessons from mission-critical database migrations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7111–7122.
12. Navandar, P. (2022). Adaptive SAP security control framework for ML driven anomaly detection, role based access hardening, and continuous compliance monitoring in SAP S/4HANA environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(3), 4939–4952. https://doi.org/10.15662/IJEETR.2022.0403005
13. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
14. 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
15. Shewale, V. (2022). Third-Party and Supply Chain Risk in Oil & Gas. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9596.
16. Parasa, M. (2020). Control-mapped AI governance for high-risk HR decisions in SAP SuccessFactors: Audit-ready metrics for recruiting, performance calibration, and internal mobility. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(2), 153–168. https://doi.org/10.18090/samriddhi.v12i02.15
17. Juvvadi, R. R. (2018). Continuous accounting: Toward a real-time financial reporting architecture for the modern enterprise. Computer Fraud & Security, 2018(12), 33–41.
18. Boddupally, H. L. (2021). A telemetry-centric approach to identifying recurrent defect structures in software systems. Available at SSRN 6270478.
19. Chettiyar, S. S. S. (2023). A vendor-neutral omnichannel conversational payment architecture for conversational commerce integrating BYOP, native solutions, and PCI compliance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8124–8135. https://doi.org/10.15662/IJRPETM.2023.0601012
20. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of "big data" on cloud computing: Review and open research issues. Information Systems, 47, 98–115. https://doi.org/10.1016/j.is.2014.07.006
21. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2020). Explain ability and interpretability in machine learning models. Journal of Computer Science Applications and Information Technology, 5(1), 1-7.
22. Sarngadharan, S. (2023). Federated data pipelines enabling continuous contract and asset state traceability. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8114–8123. https://doi.org/10.15662/IJRPETM.2023.0601011
23. Mannem, S. (2023). Intelligent service behavior analysis for early cyber threat prediction. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8077–8088. https://doi.org/10.15662/IJRPETM.2023.0601008
24. Govindan, V. (2023). AI-powered optimization of non-production environments: Turning constraints into business value. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8089–8104. https://doi.org/10.15662/IJRPETM.2023.0601009
25. Gandikota, S. P. (2023). An elastic cloud-native framework for processing millions of IoT events per second in smart grid environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8049–8063. https://doi.org/10.15662/IJRPETM.2023.0601006
26. Syed, S. (2023). A GxP-compliant integrated ERP framework for synchronizing OPM, SCM, and quality lab systems in pharmaceutical manufacturing. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8064–8076. https://doi.org/10.15662/IJRPETM.2023.0601007
27. Chenna, S. (2023). Solution-led integration architecture in Oracle EBS: A dual case study from foundational enterprise engagements. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8105–8113. https://doi.org/10.15662/IJRPETM.2023.0601010
28. Juvvadi, R. R. (2018). Continuous accounting: Toward a real-time financial reporting architecture for the modern enterprise. Computer Fraud & Security, 2018(12), 33–41.
29. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
30. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
31. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
32. Raj, A. A., & Sugumar, R. (2022, December). Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-7). IEEE.
33. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
34. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing (Special Publication 800-145). National Institute of Standards and Technology.
35. Chen, Y., Li, C., & Wang, H. (2022). Big data and predictive analytics for business intelligence: A bibliographic study (2000–2021). Forecasting, 4(4), 767–786. https://doi.org/10.3390/forecast4040042
36. Oliveira, F., Suneja, S., Nadgowda, S., Nagpurkar, P., & Isci, C. (2017). OpVis: Extensible, cross-platform operational visibility and analytics for cloud. Proceedings of the 18th International Middleware Conference, 1–13.