International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

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

Neeraj KumarFaculty of Science, Dayalbagh Educational Institute, Dayalbagh, Agra, IndiaGovind KushwahFaculty of Science, Dayalbagh Educational Institute, Dayalbagh, Agra, IndiaPreetvanti SinghFaculty of Science, Dayalbagh Educational Institute, Dayalbagh, Agra, India

Vol 10 No 1 (2026): Volume 10, Issue 1, January 2026 | Pages: 1-19

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 06-01-2026

doi Logo doi.org/10.47001/IRJIET/2026.101001

pdf iconFull Text PDF
Abstract

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.

Keywords

Data warehouse, multi-dimensional analysis, music data, OLAP cubes, MDX queries


Citation of this Article

Neeraj Kumar, Govind Kushwah, & Preetvanti Singh. (2026). Multidimensional Analysis Integrated with Multicriteria Decision-making in Data Warehouse Framework. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(1), 1-19. Article DOI https://doi.org/10.47001/IRJIET/2026.101001

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Patel, S. (2021). Data warehousing fundamentals: A comprehensive study. ResearchGate. https://www.researchgate.net/publication/354680771
  2. Nambiar, A., & Mundra, D. (2022). An overview of data warehouse and Data Lake in modern enterprise data management. Information, 6(4), 132. https://www.mdpi.com/2504-2289/6/4/132
  3. De Witte, M., Pinho, A. D. S., Stams, G. J., Moonen, X., Bos, A. E., & Van Hooren, S. (2022). Music therapy for stress reduction: a systematic review and meta-analysis. Health psychology review, 16(1), 134-159.
  4. Nwokenna, E. N., Sewagegn, A. A., & Falade, T. A. (2022). Effect of educational music intervention on emotion regulation skills of first-year university music education students. Medicine, 101(47), e32041.
  5. Feng, Y., & Wang, M. (2025). Effect of music therapy on emotional resilience, well-being, and employability: a quantitative investigation of mediation and moderation. BMC psychology, 13(1), 47.
  6. Li, M., & Zhang, L. (2025). Research on the optimisation of music education curriculum content and implementation path based on big data analysis. Applied Mathematics & Nonlinear Sciences, 10(1).
  7. Wang, X., & Wang, T. (2023). Construction and application of a big data analysis platform for college music education for college students' mental health. International Journal of Data Warehousing and Mining (IJDWM), 19(4), 1–16.
  8. Aasim, S. (2024). Harmonizing sleep: Exploring the multifaceted role of music therapy in sleep health. Chemotherapy, 24(3), 360-369.
  9. Chatterjee, D. (2024). The Role of Music Therapy in Stress Reduction. International Journal for Multidisciplinary Research (IJFMR), 6(2).
  10. Abrarov, R. D., & Khudaybergenov, T. A. (2024, November). Using OLAP Cubes as Dataset for Neural Networks: Integrating Business Intelligence and Artificial Intelligence. In 2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE) (pp. 1600-1604). IEEE.
  11. Ahmadi, S. (2023). Optimizing data warehousing performance through machine learning algorithms in the cloud. International Journal of Science and Research, 12(12), 1859–1867.
  12. Szabat Fernández, A. (2024). Diseño de un almacén de datos para análisis de gustos y hábitos musicales.
  13. Lado Río, C. (2024). Perfil completo de artistas musicais: integrando información de Spotify e MusicBrainz.
  14. Bouaziz, S., Nabli, A., & Gargouri, F. (2021). Towards data warehouse from open data: Case of COVID-19. International Journal of Hybrid Intelligent Systems, 17(3-4), 129-142.
  15. Padron-Uy, J., Parihar, A., Gagnier, K., Hains, G., & Wong, A. (2025). Scaling a data warehouse system with machine learning integration for stock market forecasting. Authorea Preprints.
  16. Martinez-Mosquera, D., Navarrete, R., Luján-Mora, S., Recalde, L., & Andrade-Cabrera, A. (2024). Integrating OLAP with NoSQL databases in big data environments: Systematic mapping. Big Data and Cognitive Computing, 8(6), 64.
  17. Bouaziz, S., Boukettaya, S., Nabli, A., & Gargouri, F. (2025). A formal algebra for document-oriented NoSQL data warehouses: formalisation and evaluation. Cluster Computing, 28(3), 170.
  18. Fan, X., & Lu, J. (2024). Enterprise-level data warehouse system based on Hive in big data environment. Procedia Computer Science, 243, 67–75.
  19. Djiroun, R., Guessoum, M. A., & Benkhelifa, E. H. (2024, December). An OLAP internal/external cube recommendation approach based on artificial intelligence techniques. In Global Congress on Emerging Technologies (GCET-2024) (pp. 145–152). IEEE.
  20. Kumar, N., Kushwah, G., & Singh, P. (2025). Developing a music data warehouse for determining audience preferences. In Progressive Computational Intelligence, Information Technology and Networking (pp. 217-223). CRC Press.
  21. Rana, M. (2025). Overview of data warehouse architecture, big data and green computing.
  22. Kimball, R., & Ross, M. (2010). The Kimball group reader: relentlessly practical tools for data warehousing and business intelligence. John Wiley & Sons.
  23. Lyu, S., Craig, S., O'Reilly, G., & Taniar, D. (2025). The development and use of data warehousing in clinical settings: a scoping review. Frontiers in Digital Health, 7, 1599514.
  24. Chilukoori, S. S. R., Gangarapu, S., & Kadiyala, C. K. DATA WAREHOUSING IN THE FINANCIAL SERVICES INDUSTRY: CHALLENGES, OPPORTUNITIES, AND REGULATORY CONSIDERATIONS.
  25. Maaitah, T. (2023). The Role of Business Intelligence Tools in the Decision Making Process and Performance. Journal of intelligence studies in business, 13(1).
  26. Güneş, İ., & Birgin, M. K. (2023). Implementing data warehouse infrastructure for an e-learning system. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 13(3), 750-766.
  27. Yadav, U., Bhandari, H. L., & Poudel, S. Implementation of Data Warehouse Technology in Academic Data Management.
  28. Sanjeetha, M. B. F., & Asanka, D. (2025). Design and Implementation of Data Warehousing for Small and Medium Sized Enterprises (SMEs): A Cost-Effective Approach in Online Stores. Asian Journal of Computer Science and Technology, 14(1), 57-65.
  29. Navarro, J., Gaujoux, V., Ouimet, M. C., Ferreri, L., & Reynaud, E. (2023). How does background music affect drivers’ behaviours, emotions and mood behind the wheel?. Transportation research part F: traffic psychology and behaviour, 98, 47-60.
  30. Xian, X., Zhang, X., Zheng, D., & Wang, Y. (2024). Mental Health Benefits of Listening to Music During COVID-19 Quarantine: Cross-Sectional Study. JMIR Formative Research, 8(1), e46497.
  31. Feng, Y., & Wang, M. (2025). Effect of music therapy on emotional resilience, well-being, and employability: a quantitative investigation of mediation and moderation. BMC psychology, 13(1), 47.
  32. Yingjie, F., & Mingda, W. (2024). Effect of Music Therapy on Emotional Resilience, Well-Being, and Employability: A Quantitative Investigation of Mediation and Moderation.
  33. Najm, I. A., Dahr, J. M., Hamoud, A. K., Alasady, A. S., Awadh, W. A., Alshawki, M. B., & Humadi, A. M. (2022). OLAP mining with educational data mart to predict students’ performance. Informatica, 46(5).
  34. Alkhanifer, A., & AlZubi, A. A. (2024). Big data-based query optimization for business intelligence. Intelligent Data Analysis, 1088467X251331665.
  35. Lyu, S., Craig, S., O'Reilly, G., & Taniar, D. (2025). The development and use of data warehousing in clinical settings: a scoping review. Frontiers in Digital Health, 7, 1599514.
  36. Lv, Y. (2025). Research on the Application of Data Warehouse in Education Evaluation System. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 19(1), 1-13.
  37. Cormier, K., Padron-Uy, J., Wong, A., Gagnier, K., & Parihar, A. (2025). Data Warehouse Design for Multiple Source Forest Inventory Management and Image Processing. arXiv preprint arXiv:2502.07015.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.