2021· International Journal of Data Engineering and Intelligent Computing· Vol 4, pp. 01-12· 0 citations
TL;DR
An architecture for integrating semantic mediation is proposed, an architecture for integrating semantic mediation is discussed, best practices are discussed, and performance, scalability, and interoperability are evaluated.
Abstract
In the era of digital transformation, organizations are increasingly leveraging multi-cloud strategies to harness the power of diverse cloud service providers. However, the heterogeneity of data schemas, storage formats, and semantic models across cloud platforms presents significant challenges for unified data analysis. Semantic data transformation offers a robust approach to harmonizing disparate datasets by mapping them to a common ontology or semantic model. This paper explores the methodologies, frameworks, and tools enabling semantic data transformation in multi-cloud data warehousing environments. It proposes an architecture for integrating semantic mediation, discusses best practices, and evaluates performance, scalability, and interoperability. Case studies and experimental results demonstrate the practical viability and impact of semantic transformation in achieving data consistency, governance, and advanced analytics across cloud platforms.
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