Abstract
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.
Highlights
Business Intelligence (BI) is a vast category of methods, applications and technologies of gathering data, accessing information, and analyzing a large amount of data for getting knowledge in the organization to make effective business decisions and made Information Technology (IT) measurements powerful
At first step weights and bias are defined for multi-layer perceptron and learning with MLP occurred
Crossover and mutation in genetic algorithm occurs and offspring is placed into new population
Summary
Business Intelligence (BI) is a vast category of methods, applications and technologies of gathering data, accessing information, and analyzing a large amount of data for getting knowledge in the organization to make effective business decisions and made Information Technology (IT) measurements powerful. The size of the data warehouse and complexity of the queries can produce long queries that require much time for completion This causes delays that are not acceptable in most decision support system environments because it reduces the efficiency of the system. Cube analysis allows users to explore data by calculating the aggregate size of all possible groups as defined by their new dimensions. A cube is a multi-dimensional redundancy plan relationship that calculates all SQL-grouped operators and aggregates their results in n-dimensional space to answer OLAP queries. This aggregation is calculated in derivative summary tables or multi-dimensional arrays and mathematical tools are required to estimate the aggregation sizes. The values in each cell of a data cube contain a measuring value [4, 23, 30, 35]
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