Impact of Artificial Intelligence on the Generation Process of the Data Warehouse Model

This article explores the intersection of data warehousing (DW) and artificial intelligence (AI) techniques, aiming to address the challenges posed by escalating data volumes and the imperative for informed decision-making. In the "Related Work" section, foundational insights into DW development methodologies are provided, alongside discussions on AI's burgeoning role in augmenting warehouse management. The limitations of existing solutions, particularly regarding data accuracy and human error, are highlighted. Moving to the "Background Knowledge" section, a comprehensive understanding of DW fundamentals and AI applications in warehouse management is presented. Key characteristics of DWs are elucidated, highlighting their critical function in supporting data-driven decision-making. The evolving significance of AI in optimizing warehouse operations and enhancing data quality is explored. Finally, in "Our Proposition," novel methodologies and approaches for advancing DWsystems through AI integration are delineated. The article delves into the incorporation of AI into the Extract, Transform, Load (ETL) process, highlighting its potential in intelligent data modeling, automated data cleansing, and continuous data quality monitoring. The myriad benefits of AI in DWs are underscored, emphasizing its crucial role in optimizing operations and facilitating informed decision-making in contemporary enterprises.