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Analysis Study Research Paper | Computer Science | India | Volume 14 Issue 1, January 2025 | Popularity: 5 / 10

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The Advantages of Using Multi - Dimensional Databases: A Comprehensive Analysis

(C) Dr Ashok Jahagirdar

Abstract: The increasing complexity of data in modern organizations demands advanced database systems capable of handling vast amounts of information efficiently while facilitating swift and intuitive analysis. Multi - dimensional databases (MDDBs) are specifically designed to meet these needs by organizing data into dimensions and measures, enabling businesses to perform complex analytical queries with ease. This paper explores the fundamental advantages of MDDBs, such as faster query processing, enhanced scalability, and improved data visualization, compared to traditional relational databases. MDDBs 1) reduce query processing times by up to 70% 2) improve reporting speed by 50%, and 3) achieve compression rates of up to 40%, making them indispensable for data - driven industries. Additionally, the paper highlights industry applications in retail, finance, and healthcare, showcasing how MDDBs are transforming business intelligence and decision - making processes. Despite some initial implementation challenges, the benefits of multi - dimensional databases in analytical environments outweigh their limitations, positioning them as a critical tool for the future of data management and analysis. Multi - dimensional databases (MDDBs) have become a cornerstone in modern data analysis, particularly in the context of data warehousing and online analytical processing (OLAP). Unlike traditional relational databases, MDDBs organize data in a way that is intuitive for analysis and reporting. This paper delves into the advantages of multi - dimensional databases, supported by numerical statistics, and highlights their applications in business intelligence, financial forecasting, and decision - making systems.

Keywords: Multi - dimensional databases (MDDBs), Data analytics, Business intelligence (BI), Online analytical processing (OLAP), Data modeling, Dimensional data, Database performance, Data visualization, Query optimization,

Edition: Volume 14 Issue 1, January 2025

Pages: 1149 - 1154

DOI: https://www.doi.org/10.21275/SR25126111728

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