A Grey Wolf Optimization (GWO) based Cube Selection in OLAP Data Model

Abstract

The data cube assessments dependent on Online Analytical Processing (OLAP) trouble for numerous depositing splendors over broad information. In favor of appreciating question answering era pleasant with OLAP skeleton patrons and allowing complete industry organized notice compulsory, OLAP information is organized as a data cube model. The OLAP questions are answered in rapid and sturdy time by exploiting the cube embodiment for appraisals buyers. Until now this moreover insets insupportable charge, concerning to accumulation remembrance and time, yet as a data storage area had a typical length and extent which will be influential on stimulating procedure. Thus, cube classification has visited to be refined fascinating to moderate question managing charge, preserving as a control the materializing breach. Numerous strategies and heuristics like divergence and voracious approaches have been exploited to suggest a vague solution. Here, a Grey Wolf Optimization (GWO) strategy is exploited in a lattice structure for finding the best data cube to decrease the question processing charge. The outputs describe the superior efficiency of GWO against GA, PSO and ALO based on total dimensions and frequency.