Cloud-Native Data Ecosystems for Predictive Industrial Decisions
Keywords:
Cloud Native, Industrial Systems, Predictive Modeling, Data Ecosystems, Data Pipelines, Machine Learning, ML Lifecycle, Feature Stores, Real Time, Model Inference, Data Processing, Workflow Orchestration, Event Driven, Scheduling Systems, Cloud Computing, Data Infrastructure, Model Deployment, Decision Systems, Scalable Systems, Analytics Systems.Abstract
Industrial organizations are rapidly adopting cloud technologies to reduce infrastructure management costs and increase operational flexibility. Nonetheless, existing cloud deployment uses are mainly focused on infrastructure as a service and data storage. In parallel, the demand for data-driven predictive modeling has grown and continues to evolve beyond standard business intelligence. As a result, cloud-native software development and deployment models have attracted significant attention from academia and industry. However, related research into the cloud-native paradigm remains limited and tends to offer design and development support for a single application or service without addressing the complete data ecosystem.
The data processing and predictive modeling lifecycle requires a cloud-native architecture that supports large-scale predictive modeling operations and the construction of a data-oriented infrastructure. Building intelligent capabilities involves orchestrating all machine learning operations in the cloud, integrating advanced machine learning lifecycle frameworks, incorporating feature stores, and enabling real-time scoring orchestration and execution algorithms. A predictive decision-making data ecosystem focuses on the complete data processing pipeline in relation to predictive models, integrating model data processing and inference pipelines into orchestration and scheduling frameworks while supporting event-driven workflows.
References
1. Aheleroff, S., Xu, X., Lu, Y., Aristizabal, M., Velásquez, J. P., Joa, B., & Valencia, Y. (2020). IoT-enabled smart appliances under Industry 4.0: A case study. Advanced Engineering Informatics, 43, 101043.
2. Foidl, H., & Felderer, M. (2023). An approach for assessing industrial IoT data sources to determine their data trustworthiness. Internet of Things, 22, 100735.
3. Kolla, S. H., & Loganathan, R. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research, 603-618.
4. Kumar, R., & Agrawal, N. (2023). Analysis of multi-dimensional Industrial IoT (IIoT) data in Edge–Fog–Cloud based architectural frameworks: A survey on current state and research challenges. Journal of Industrial Information Integration, 35, 100504.
5. Deng, S., Zhao, H., Huang, B., Zhang, C., Chen, F., Deng, Y., Yin, J., Dustdar, S., & Zomaya, A. Y. (2023). Cloud-native computing: A survey from the perspective of services. Journal of Systems Architecture, 146, 103019.
6. Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
7. Pandey, N. K., Kumar, K., Saini, G., & Mishra, A. K. (2023). Security issues and challenges in cloud of things-based applications for industrial automation. Annals of Operations Research.
8. Raja Sree, T. (2022). Role of fog-assisted Industrial Internet of Things: A systematic review. Transactions on Emerging Telecommunications Technologies, 33(12), e4611.
9. Pokhrel, S. R. (2022). Learning from data streams for automation and orchestration of 6G Industrial IoT: Toward a semantic communication framework. Neural Computing and Applications, 34(17), 15197–15206.
10. Kolla, S. K. (2022). Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5370-5380.
11. Hästbacka, D., Halme, J., Barna, L., Hoikka, H., Pettinen, H., Larranaga, M., Bjorkbom, M., Mesia, H., Jaatinen, A., & Elo, M. (2020). Dynamic edge and cloud service integration for Industrial IoT and production monitoring applications of industrial cyber-physical systems. Journal of Industrial Information Integration, 18, 100153.
12. Pop, P., Zarrin, B., Barzegaran, M., Schulte, S., Punnekkat, S., Ruh, J., & Steiner, W. (2020). The FORA fog computing platform for Industrial IoT. IEEE Access, 8, 203301–203321.
13. Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
14. Radanliev, P., De Roure, D., Page, K., Nurse, J. R. C., Montalvo, R. M., Santos, O., Maddox, L., & Burnap, P. (2020). Cyber risk at the edge: Current and future trends on cyber risk analytics and artificial intelligence in the Industrial Internet of Things and Industry 4.0 supply chains. Cybersecurity, 3(13).
15. Yu, W., Dillon, T., Mostafa, F., Rahayu, W., & Liu, Y. (2020). A global manufacturing big data ecosystem for fault detection in predictive maintenance. IEEE Transactions on Industrial Informatics, 16(1), 183–192.
16. Villalobos, K., Ramírez-Durán, V. J., Diez, B., Blanco, J. M., Goñi, A., & Illarramendi, A. (2020). A three-level hierarchical architecture for efficient storage of Industry 4.0 data. Computers in Industry, 121, 103257.
17. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
18. Wei, W., Yuan, J., & Liu, A. (2020). Manufacturing data-driven process adaptive design method. Procedia CIRP, 91, 728–734.
19. Parimala, M., Priya, R. M. S., Pham, Q. V., Dev, K., Maddikunta, P. K. R., Gadekallu, T. R., & Huynh-The, T. (2021). Fusion of federated learning and Industrial Internet of Things: A survey. IEEE Internet of Things Journal, 8(18), 14339–14374.
20. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
21. Zellinger, W., Wieser, V., Kumar, M., Brunner, D., Shepeleva, N., Gálvez, R., Langer, J., Fischer, L., & Moser, B. (2021). Beyond federated learning: Confidentiality-critical machine learning applications in industry. IEEE International Symposium on Multimedia, 734–743.
22. Wang, C., Zhu, Y., Shi, W., Chang, V., Vijayakumar, P., Liu, B., Mao, Y., Wang, J., & Fan, Y. (2020). A dependable time-series analytic framework for cyber-physical systems of IoT-based smart grid. ACM Transactions on Cyber-Physical Systems, 4(1), 1–18.
23. Sharma, P., Chen, M., & Park, J. H. (2020). A software-defined fog node architecture for Industrial Internet of Things. IEEE Communications Magazine, 58(7), 86–92.
24. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
25. Xu, L. D., Xu, E. L., & Li, L. (2020). Industry 4.0: State of the art and future trends. International Journal of Production Research, 58(10), 2941–2962.
26. Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Gonzalez, E. S. (2021). Understanding the adoption of Industry 4.0 technologies in manufacturing. Sustainable Operations and Computers, 2, 275–286.
27. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2020). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 17(4), 2405–2415.
28. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
29. Leng, J., Ruan, G., Jiang, P., Xu, K., Liu, Q., Zhou, X., & Liu, C. (2021). Blockchain-enabled smart manufacturing. Journal of Manufacturing Systems, 60, 124–137.
30. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2020). Digital twin in manufacturing: A categorical literature review. IFAC-PapersOnLine, 53(2), 2429–2435.
31. Qi, Q., & Tao, F. (2022). Digital twin and big data toward smart manufacturing. Engineering, 8, 100–112.
32. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248-260.
33. Lu, Y. (2020). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10.
34. Zhang, Y., Ren, S., Liu, Y., & Si, S. (2020). A big data analytics architecture for cleaner manufacturing. Journal of Cleaner Production, 247, 119117.
35. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
36. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry. Journal of Cleaner Production, 278, 123414.
37. Wang, S., Wan, J., Li, D., & Zhang, C. (2020). Implementing smart factory of Industry 4.0. International Journal of Distributed Sensor Networks, 16(6).
38. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
39. Khan, W. Z., Rehman, M. H., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2020). Industrial Internet of Things: Recent advances and enabling technologies. Future Generation Computer Systems, 107, 1034–1046.
40. Ali, S., Gupta, R., Nayyar, A., & Kumar, N. (2021). Blockchain for Industrial Internet of Things. IEEE Network, 35(2), 132–139.
41. Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1-14.
42. Yang, H., Kumara, S., Bukkapatnam, S., & Tsung, F. (2020). The Internet of Things for smart manufacturing. IISE Transactions, 52(11), 1190–1210.
43. Li, X., Xu, L. D., & Zhao, S. (2021). 5G Internet of Things: A survey. Journal of Industrial Information Integration, 10, 1–9.
44. Chen, Y., & Zhang, Y. (2022). AI-enabled predictive maintenance in smart manufacturing. Robotics and Computer-Integrated Manufacturing, 73, 102222.
45. Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
46. Devarasetty, N. (2023). Scalable data engineering approaches for AI-driven Industrial IoT applications. International Journal of Scientific Research and Management, 11(6), 954–968.
47. Behnke, I., & Austad, H. (2023). Real-time performance of Industrial IoT communication technologies: A review. IEEE Access, 11, 119520–119548.
48. Kaur, K., Garg, S., & Kaddoum, G. (2022). Machine learning for Industrial Internet of Things. IEEE Network, 36(2), 158–165.
49. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
50. Li, B., Hou, B., Yu, W., Lu, X., & Yang, C. (2020). Applications of artificial intelligence in intelligent manufacturing. Frontiers of Information Technology & Electronic Engineering, 21(4), 558–571.
51. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2020). Smart manufacturing and cloud technologies. Procedia CIRP, 97, 491–496.
52. Zhou, K., Liu, T., & Zhou, L. (2020). Industry 4.0: Towards future industrial opportunities. International Journal of Production Research, 58(10), 2963–2975.
53. Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
54. Lee, J., Davari, H., Singh, J., & Pandhare, V. (2020). Industrial AI and predictive analytics. Manufacturing Letters, 18, 20–23.
55. Sarker, I. H. (2021). Machine learning for intelligent data analysis and automation. SN Computer Science, 2(3), 160.
56. Sharma, A., & Wang, G. (2021). Big data analytics in Industry 4.0. Journal of Big Data, 8(1), 1–28.
57. Li, C., Mahadevan, S., Ling, Y., Choze, S., & Wang, L. (2020). Dynamic Bayesian network for machine prognostics. Mechanical Systems and Signal Processing, 136, 106486.
58. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
59. Zhang, C., & Tao, F. (2021). Data-driven smart manufacturing. Engineering, 7(4), 499–507.
60. Wang, L., Törngren, M., & Onori, M. (2020). Current status and advancement of cyber-physical systems. Journal of Manufacturing Systems, 37, 517–527.
61. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0. Engineering, 7(4), 530–535.
62. Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2021). Industry 4.0 technologies. Journal of Manufacturing Technology Management, 32(8), 1635–1660.
63. Bandi, V. D. V. K. Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics.
64. Sony, M., & Naik, S. (2020). Industry 4.0 integration challenges. Production Planning & Control, 31(10), 799–815.
65. Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Suman, R. (2022). Artificial intelligence applications for Industry 4.0. Advanced Industrial and Engineering Polymer Research, 5(4), 230–242.
66. Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022). Digital twin modeling. Robotics and Computer-Integrated Manufacturing, 64, 101980.
67. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
68. Mourtzis, D. (2021). Simulation in digital manufacturing. Procedia CIRP, 104, 130–135.
69. Sestino, A., Prete, M. I., Piper, L., & Guido, G. (2020). Internet of Things and Big Data as enablers. Technological Forecasting and Social Change, 149, 119757.
70. Khan, M. A., Salah, K., Jayaraman, R., Arshad, J., Omar, M., & Debe, M. (2022). Blockchain-enabled Industrial Internet of Things. Future Generation Computer Systems, 127, 290–305.
71. Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). Big data analytics in Industry 4.0. Technological Forecasting and Social Change, 173, 121178.
72. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
73. Kumar, A., Singh, R. K., Modgil, S., & Dwivedi, Y. K. (2022). Artificial intelligence for supply chain resilience. Annals of Operations Research.
74. Chui, K. T., Alhalabi, W., Pang, S. S., & Liu, R. W. (2022). Artificial intelligence and machine learning for Industry 4.0. Applied Sciences, 12(16), 8081.
75. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2021). Internet of Things: A survey on enabling technologies and applications. IEEE Communications Surveys & Tutorials, 23(2), 1223–1282.
76. Zhang, H., Li, Z., Wang, H., & Liu, Y. (2023). Edge intelligence for Industrial Internet of Things. IEEE Internet of Things Journal, 10(8), 6700–6715.
77. Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
78. Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2021). The role of big data analytics in Industrial Internet of Things. Future Generation Computer Systems, 124, 114–128.
79. Ghosh, A., Edwards, D. J., & Hosseini, M. R. (2022). Digital twins in smart manufacturing: A systematic review. Journal of Manufacturing Systems, 63, 252–267.
80. Singh, R. P., Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Smart manufacturing technologies for Industry 4.0: A review. Sustainable Operations and Computers, 4, 1–16.
Additional Files
Published
Data Availability Statement
None
