Home Archives Vol. 3 (2019) Article
Type Articles
Open Access
Peer Reviewed
Publication Date Oct 13, 2019

AI-Powered Impact Analysis for Multi-Site Schema Evolution

V
Vasudevan Ananthakrishnan Yakshna Solutions Inc, USA
M
Manish Tomar Citi, USA
P
Priya Ranjan Parida Universal Music Group, USA

Abstract

Schema evolution in distributed, multi-site database ecosystems is complex and error-prone which causes unanticipated propagation failures across the staging and production tiers. The objective of this paper is to present an AI-driven impact analysis technique that uses knowledge graph-based reasoning which combines DDL diffs and reproduce their transitive effects across related contexts. 

schema evolution AI impact analysis DDL diff simulation multi-site databases dependency propagation

How to Cite

[1]
Vasudevan Ananthakrishnan, Manish Tomar, and Priya Ranjan Parida, “AI-Powered Impact Analysis for Multi-Site Schema Evolution”, Edinburg J. of Nat. Lang. Proc. and AI, vol. 3, pp. 1–36, Oct. 2019, Accessed: Aug. 24, 2026. [Online]. Available: https://ejnlpai.org/index.php/publication/article/view/15
RIS BibTeX

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