A Genetic-Firefly Hybrid Algorithm to Find the Best Data Location in a Data Cube

Decision-based programs include large-scale complex database queries. If the response time is short, query optimization is critical. Users usually observe data as a multi-dimensional data cube. Each data cube cell displays data as an aggregation in which the number of cells depends on the number of other cells in the cube. At any given time, a powerful query optimization method can visualize part of the cells instead of calculating results from raw data. Business systems use different approaches and positioning of data in the data cube. In the present study, the data is trained by a neural network and a genetic-firefly hybrid algorithm is proposed for finding the best position for the data in the cube