Multidimensional Analysis Integrated with Multicriteria Decision-making in Data Warehouse Framework

In today's data-driven environments, due to rise in data complexity and manageability concerns, data warehouses offers to store, integrate, and analyze large volume of historical data for informed decision-making. This study presents the design and implementation of a multidimensional analysis and ranking framework for assessing artist popularity in the digital music market using a data warehouse-driven methodology. ETL processes are utilized to convert data from GitHub into a star schema-based warehouse, named MuDW, in SQL Server. This ensures that fact and dimension tables are logically normalized to facilitate multidimensional queries and insights. Microsoft Visual Studio creates an OLAP cube (MUSIC_CUBE) to support multidimensional analysis across temporal, geographical, and categorical dimensions such as artist_album, date, and location. Furthermore, the analytical results derived from OLAP are combined with the order preference by similarity to ideal solution (TOPSIS) method, and the entropy weight method (EWM) is used to generate criterion weights, to determine artist rankings based on multiple quantitative factors related to listener engagement and artist revenue. Further, a 3D OLAP cube visualization using Python is developed to show artist, genre popularity distribution over time and location. This hybrid framework effectively integrates data warehousing, OLAP processing, and multicriteria decision-making to provide actionable insights on artist popularity trends of strategic value for decision making for music platforms and industry stakeholders.