@Article{JICS-6-269,
author = {Adio Akinwale, Kolawole Adesina and Olusegun Folorunso},
title = {A Framework for Reducing Multidimensional Database to Two Dimensions},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {6},
number = {4},
pages = {269--278},
abstract = {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. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22668.html}
}