Data mining and economic forecasting in DW-based economical decision support system

Abstract

Decision demand has hierarchies for different users and decision analysis demand in various areas and fields have particularity according to different topics. Since traditional MIS is hard to meet the demand of analysis and processing of growing mass data, a novel decision support system (DSS) is urgent to be proposed for decision makers. Based on data warehouse, data mining and OLAP technology, we propose a DSS with modular design, and explain the structure and key technologies of it in this article. Our study establishes multidimensional dataset for OLAP analysis to perform slicing, dicing, drilling and rotation operation. In data mining, for the problems of large data-set such as long learning time and decreasing generalisation ability, an SVM accelerating algorithm based on boundary sample selection is put forward. The system test results demonstrate that the data mining has better prediction effects on economical forecasting. Therefore, the research has better practicability and higher accuracy, which shows certain value of popularisation and implementation.