A New Framework to Adopt Multidimensional Databases for Organizational Information System Strategies
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Abstract (arXiv)
As information becomes increasingly sizable for organizations to maintain the challenge of organizing data still remains. More importantly, the on-going process of analysing incoming data occurs on a continual basis and organizations should employ existing procedures that may not be adequate or efficient when attempting to access specific information to analyse. In these latter days of technological advancement, organizations can offer their customers extensive data resources to utilize and thus accomplish individual objectives and maintain competitiveness; however, it remains a challenge in providing data in a format that serves each clients suited needs. For some, the complexity of a data model can be overwhelming to utilize. Furthermore, companies should secure an understanding of the purchasing power used by specific consumer groups to remain competitive and ease the operation of data analysis. This research paper is to examine the use of multi-dimensional models within a business environment and how it may provide customers and managers with generating queries that will provide accurate and relevant data for effective analysis. It also provides a new framework that can aid various types of organisations using sizable database systems to create their own multidimensional model from relational databases and present the data in multidimensional views. It also defines the requirements. Despite the availability of set tools, the complexity of utilizing the conceptions discourages customers as they may become apprehensive about exploring these options for analytical purposes. This could be done by conducting a query.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 1.
5 papers.
- 2024From A-to-Z Review of Clustering Validation Indicescited by 11
- 2021Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm starcited by 4
- 2022An Improved Deep Convolutional Neural Network by Using Hybrid Optimization Algorithms to Detect and Classify Brain Tumor Using Augmented MRI Imagescited by 0
- 2024Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryptioncited by 0
- 2025Reducing Formal Context Extraction: A Newly Proposed Framework from Big Corporacited by 0
- 2025Reducing Formal Context Extraction: A Newly Proposed Framework from Big Corpora1 Apr 2025
- 2024Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption23 Jul 2024
- 2024From A-to-Z Review of Clustering Validation Indices18 Jul 2024
- 2022An Improved Deep Convolutional Neural Network by Using Hybrid Optimization Algorithms to Detect and Classify Brain Tumor Using Augmented MRI Images8 Jun 2022
- 2021Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star10 Jul 2021