Data Mesh Meets AI: Federated Ownership and ML-Enabled Contracts for Cross-Domain Data Collaboration
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I2P117Keywords:
Data Mesh, Federated Ownership, Smart Contracts, Machine Learning, Data Collaboration, AI Governance, Cross-Domain Data Sharing, Data as a Product, Federated Learning, Trust Automation, Data Contract Enforcement, Metadata Intelligence, Decentralized Data Infrastructure, Real-Time Compliance, AI-Driven Policy EnforcementAbstract
In modern data-driven companies, the problem of fragmented data continues to stifle innovation, make it harder to make these decisions, and make it harder for people from different departments to work together. As companies grow, traditional centralized data architectures have trouble growing quickly & meeting the needs of various fields. This has led to the creation of the Data Mesh paradigm, which supports a decentralized approach to data management in which domain teams own, share, and utilize data as a product. However, enabling substantial cross-domain data collaboration within a distributed framework remains a considerable challenge, particularly regarding the establishment of trust, governance & interoperability across many other domains. This essay looks at how federated ownership and AI-driven smart contracts might change the way information is shared and managed in many other different fields. By adding ML to contract logic, businesses can automate access control, usage limitations, compliance checks & predictive alerts. This reduces friction and builds trust between teams. Through case studies and architectural analyses, we show how machine learning-enabled contracts allow for secure, scalable, and auditable data exchange while keeping autonomy and avoiding bottlenecks. Our study shows that adding AI to federated data governance makes data easier to get to and better quality, and it also gives domain teams more flexibility and responsibility to come up with new ideas. The paper offers a paradigm for businesses seeking to implement Data Mesh via intelligent automation, including practical insights into the design, implementation & measurable impacts of this technique in real-world contexts.
References
1. Hechler, E., Weihrauch, M., & Wu, Y. (2023). Data fabric and data mesh approaches with AI. Apress Berkeley, Berkeley, CA, USA.
2. Kumar Doodala, A. N. (2024). Validating UX consistency Across Omnichannel Platform. American International Journal of Computer Science and Technology, 6(6), 87-97. https://doi.org/10.63282/3117-5481/AIJCST-V6I6P109
3. Dey, D., Slomka, P. J., Leeson, P., Comaniciu, D., Shrestha, S., Sengupta, P. P., & Marwick, T. H. (2019). Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. Journal of the American College of Cardiology, 73(11), 1317-1335.
4. Srigadde, B. R., & Devaraju, J. M. (2024). Building a Reusable AI Connection Utility Class. International Journal of Emerging Research in Engineering and Technology, 5(2), 188-200. https://doi.org/10.63282/3050-922X.IJERET-V5I2P119
5. McDermott, D. (1976). Artificial intelligence meets natural stupidity. ACM Sigart Bulletin, (57), 4-9.
6. Shiramalla, R. (2024). Secure Multi-Cloud API Orchestration between Salesforce, Oracle CPQ, and Azure. American International Journal of Computer Science and Technology, 6(3), 102-113. https://doi.org/10.63282/3117-5481/AIJCST-V6I3P108
7. Botsch, M., Kobbelt, L., Pauly, M., Alliez, P., & Lévy, B. (2010). Polygon mesh processing. CRC press.
8. Muppaneni, R. K. (2022). From Legacy ERP to Cloud-First: A Transformation Story with Dynamics 365. International Journal of Emerging Research in Engineering and Technology, 3(4), 153-164. https://doi.org/10.63282/3050-922X.IJERET-V3I4P117
9. Muppaneni, K., & Vejella, M. (2023). Security and Data Privacy in Redux Stores. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 153-162. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P117
10. Farahani, B., & Monsefi, A. K. (2023). Smart and collaborative industrial IoT: A federated learning and data space approach. Digital Communications and Networks, 9(2), 436-447.
11. Suryadevara, S. S. K., & Shaik, K. (2023). Real-Time Anomaly Detection and Attack Mitigation for Cloud-Based Content Delivery Paths Using AI. International Journal of Emerging Research in Engineering and Technology, 4(1), 175-185. https://doi.org/10.63282/3050-922X.IJERET-V4I1P119
12. Kishor, K. (2022). Communication-efficient federated learning. In Federated Learning for IoT Applications (pp. 135-156). Cham: Springer International Publishing.
13. Kumar Doodala, A. N. (2024). Service Virtualization for API-First development: A shift-Left Testing Strategy. American International Journal of Computer Science and Technology, 6(4), 50-58. https://doi.org/10.63282/3117-5481/AIJCST-V6I4P105
14. Parakala, A. (2024). Agentic Automation: What’s next for Jobs. American International Journal of Computer Science and Technology, 6(6), 25-35. https://doi.org/10.63282/3117-5481/AIJCST-V6I6P103
15. Ghimire, B., & Rawat, D. B. (2022). Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things. IEEE Internet of Things Journal, 9(11), 8229-8249.
16. Allenki, S. S. (2022). Securing Databases in the Cloud with RBAC and Encryption Best Practices. International Journal of Emerging Research in Engineering and Technology, 3(3), 173-182. https://doi.org/10.63282/3050-922X.IJERET-V3I3P117
17. Vppalapati, M. (2022). The Storage Stack Nobody Draws: Cabling, Panels, and the Illusion of Isolation. International Journal of Emerging Research in Engineering and Technology, 3(2), 211-220. https://doi.org/10.63282/3050-922X.IJERET-V3I2P121
18. Manias, D. M., Shaer, I., Yang, L., & Shami, A. (2021, December). Concept drift detection in federated networked systems. In 2021 IEEE global communications conference (GLOBECOM) (pp. 1-6). IEEE.
19. Muppaneni, K. (2023). Virtual DOM vs Real DOM: Performance Benchmarks. International Journal of AI, BigData, Computational and Management Studies, 4(4), 180-189. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P118
20. Shiramalla, R. (2023). Optimizing Cross-Platform Enterprise Integrations Using Workato: A Case Study of Salesforce and Oracle SaaS Applications. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 232-243. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P124
21. De Martino, V., & Palomba, F. (2023). Classification, challenges, and automated approaches to handle non-functional requirements in ml-enabled systems: A systematic literature review. arXiv preprint arXiv:2311.17483.
22. Gaddam, R. R. (2022). Advanced Data & Model Drift Detection at Scale. International Journal of AI, BigData, Computational and Management Studies, 3(2), 124-136. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P113
23. Takkalapally, D. (2024). ShiftLeft-AI: Machine Learning Framework for Proactive Performance Assurance in CI/CD Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 285-296. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P126
24. Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., & Tari, Z. (2023). Blockchain-based federated learning for securing internet of things: A comprehensive survey. ACM Computing Surveys, 55(9), 1-43.
25. Muppaneni, R. K. (2022). Data Privacy in the Age of AI: How Dynamics 365 Handles Regulatory Challenges. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 159-170. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P117
26. Srigadde, B. R. (2024). Agents, LLMs, and Salesforce with Multi-Cloud Provider (MCP). International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 277-288. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P127
27. Radhika, K., Alla, B. S. R., Mamidi, S. B., & Puppala, N. (2022, December). A Review on Federated Learning-Based Network Intrusion Detection System. In International Conference on Information and Management Engineering (pp. 707-713). Singapore: Springer Nature Singapore.
28. Suryadevara, S. S. K., & Nakirikanti, S. (2023). Privacy-Preserving Personalization Using Federated Learning in AEM. International Journal of AI, BigData, Computational and Management Studies, 4(4), 190-199. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P119
29. Parakala, A. (2024). Self‑Learning Bots & Cloud‑Native Platforms. International Journal of Emerging Trends in Computer Science and Information Technology, 5(4), 132-141. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P114
30. Hong, C. S., Khan, L. U., Chen, M., Chen, D., Saad, W., & Han, Z. (2021). Federated learning for wireless networks (pp. 3-239). Singapore: Springer.
31. Allenki, S. S., & Lee, N. (2022). Performance Tuning Cloud-Hosted Databases: Resource Allocation & Query Optimization. International Journal of AI, BigData, Computational and Management Studies, 3(4), 152-163. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P116
32. Khan, R. S., Sirazy, M. R. M., Das, R., & Rahman, S. (2022). An ai and ml-enabled framework for proactive risk mitigation and resilience optimization in global supply chains during national emergencies. Sage Science Review of Applied Machine Learning, 5(2), 127-144.
33. Takkalapally, D., & Takkellapally, M. R. (2024). AI-SynPerf: Synthetic Data Intelligence Framework for 5G Mobile Performance Simulation. International Journal of Emerging Trends in Computer Science and Information Technology, 5(1), 182-194. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P118
34. De Martino, V., & Palomba, F. (2023). Classification, challenges, and automated approaches to handle non-functional requirements in ml-enabled systems: A systematic literature review. arXiv preprint arXiv:2311.17483.
35. Vppalapati, M., & Talasila, P. K. (2022). Correlated Independence: Why Redundant Storage Systems Share the Same Fate. International Journal of Emerging Trends in Computer Science and Information Technology, 3(1), 169-179. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P119
36. Gaddam, R. R. (2022). Cost-Aware Autoscaling for Batch vs. Online Inference. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 134-143. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P113
37. Mitra, A., Bera, B., Das, A. K., Jamal, S. S., & You, I. (2023). Impact on blockchain-based AI/ML-enabled big data analytics for Cognitive Internet of Things environment. Computer Communications, 197, 173-185.
38. Taluri, R. (2024). A Hybrid Data Engineering and Generative AI Architecture for Intelligent Data Governance, Metadata Management, and Automated Data Quality Assessment on AWS. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(2), 230-240. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P126
