Purpose Despite the increasing role of the data warehouse as a supportive decision-making
tool in today's business world, academic research for measuring its effectiveness has been …

This comprehensive overview explores the integration of machine learning (ML) in data
warehousing, focusing on optimization challenges, methodologies, results, and future …

Optimizing large-scale data processing has become crucial in the area of data management
due to the constantly growing quantity and complexity of data. Big data analysis involves …

Data growth is increasing day by day, so the data stored in the data warehouse is
increasingly piling up. When data is displayed on the dashboard or information system …

This paper introduces the EGTL (extract, generate, transfer, load) model, a theoretical
framework designed to enhance the traditional ETL processes by integrating a novel …

The query optimization process is the big challenge of most of decision support systems
around the world, such systems based on data warehouse structure to store all data to be …

A comprehensive framework for an Extract, Transform, Load (ETL) pipeline is developed
with the use of Apache Airflow, Docker, and Azure services. The study identifies gaps in …

The query optimization process is the big challenge of most of decision support systems
around the world, such systems based on data warehouse structure to store all data to be …

Cloud computing is rapidly expanding because it allows users to save the development and
implementation time on their work. It also reduces the maintenance and operational costs of …

In most key-value stores, data access patterns are typically non-uniform and display
skewness, resulting in the presence of hotkeys and coldkeys. The caching mechanism of …