AI-Assisted Reference Data Harmonization Across Global Data Center Systems

Authors

  • Sreenivasulu Naidu Yalakaturi

Keywords:

Reference Data, AI-Assisted Harmonization, Data Harmonization Workflows, Data Governance, Data Lineage, Data Sovereignty, Access Control, Predictive Analytics, Risk Management, Multi-Region Data Centers.

Abstract

Formal and independent evidence lays bare the complexity ofReference Data, distinguished from Master Data, Dimensional Data and
Metadata. While harmonization aligns disparate Data Sources within andbetween Organizations, Data Native to one Organization becomes the Standardagainst which Data from others is Normalized. Sovereignty, Privacy, Regulatory and Risk Management Policies therefore inform the Architecture of AI-AssistedHarmonization Workflows and the Design of Harmonized Data Repositories.

References

1. Paganelli, M., Del Buono, F., Baraldi, A., & Guerra, F. (2022). Analyzing how BERT performs entity matching. Proceedings of the VLDB Endowment, 15(8), 1726–1738.

2. Reddy, V. A. R. (2023). Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7979-7992

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Published

2024-08-20

How to Cite

Sreenivasulu Naidu Yalakaturi. (2024). AI-Assisted Reference Data Harmonization Across Global Data Center Systems. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 9576–9587. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5790

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Articles