TY - JOUR T1 - A Framework for Reducing Multidimensional Database to Two Dimensions AU - Adio Akinwale, Kolawole Adesina and Olusegun Folorunso JO - Journal of Information and Computing Science VL - 4 SP - 269 EP - 278 PY - 2024 DA - 2024/01 SN - 6 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22668.html KW - Matrix decomposition algorithm, multidimensional database, genetic epistatis, principal component analysis, project pursuit method, relational model AB - This work used a method of Matrix Decomposition Algorithm to obtain a new dataset of genetic epistasis as a surrogate for a multidimensional dataset which transformed multidimensional database to a 2- dimensional database. It employed decomposition algorithms based on Boyce Codd Normal Form for minimizing anomalies. The decomposition and reversible algorithms were used on relationship among object attributes and were implemented. The implemented program ran on sample genetic epistasis datasets of up to 10 dimensions and it was shown that multidimensional datasets can be reduced to two dimensions. It was established that the time taken to generate a sequence of tuples from multidimensional database to a 2- dimensional dataset was directly proportional to the number of genes considered. The result showed that the reduced 2-dimensional database did not require any in-built functions which take long processing time for generating query result as against querying of multidimensional dataset. The reduced 2-dimensional dataset was reversible to the original multidimensional dataset for lossless join operation which indicated that there was no loss of data values or tuple. The method was compared with existing reduction techniques and it was found that data access was very fast with decomposition algorithm than relational model.

Adio Akinwale, Kolawole Adesina and Olusegun Folorunso. (2024). A Framework for Reducing Multidimensional Database to Two Dimensions. Journal of Information and Computing Science. 6 (4). 269-278. doi: