Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
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 multi-criteria decision-making to provide
actionable insights on artist popularity trends of strategic value for decision
making for music platforms and industry stakeholders.
Country : India
IRJIET, Volume 10, Issue 1, January 2026 pp. 1-19