Improve the performance of multidimensional data for OLAP by using an optimization approach

The performance of query processing over OLAP (Online Analytical Processing) model is decreased due to higher query access time for huge multidimensional data. Therefore, the clustering is introduced to improve the OLAP model efficiency by getting quick query processing because of dividing the large...

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Bibliographic Details
Main Authors: Yadav, Anjana, Singh, Balveer
Format: Conference Proceeding
Language:English
Subjects:
Access time
Ant colony optimization
Centroids
Clustering
Mathematical models
Multidimensional data
Online analytical processing
Parameters
Performance enhancement
Queries
Query processing
Run time (computers)
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Summary:The performance of query processing over OLAP (Online Analytical Processing) model is decreased due to higher query access time for huge multidimensional data. Therefore, the clustering is introduced to improve the OLAP model efficiency by getting quick query processing because of dividing the large data into various clusters. The K-Means is a famous technique of clustering the data into groups to solve various clusters. The K-Means is a famous technique of clustering the data into groups to solve various real life issues. However, K-Means has some drawbacks like sensitivity to primary centroid assortment in cluster and local optimum convergence. Hence, a KMeans-Salp Swarm Optimization based Clustering (K-SSOC) is implemented to improve the performance of K-Means by providing optimal clustering over huge OLAP multidimensional data. The outcomes are obtained on MATLAB 2019a environment based on the parameter purity index, standard deviation, F-measure, intra-cluster distance and running time complexity over 1000 iterations. The results illustrate the superior performance of K-SSOC against K-Means, ACO and PSO over total six multidimensional datasets based on parameters.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0132474