Establishment Of Project Decision Support System Based Data Warehouse

Data Warehouse (DW) and Data Mining (DM) are two new kinds of decision support techniques .DW is different from Database (DB) in the way of saving data, DW reorganizes the data which is extracted from DB in order to the demand of decision, and saves it by special mine degree. DM is core technique in Knowledge Discovery in Database (KDD), and it developed from Machine Learning (ML) of Artificial Intelligence (AI). On Line Analytical Processing (OLAP) can offer multi-dimension data for improving the efficiency of processing of data. The decision support system based DW, DM and OLAP makes decision more efficient.The project DW reorganized the data which is extracted from business project DB and external environment to compose 5 subjects: project, designer, constructor, supplier and supervisor. The same subject data combine by the key and the subjects' relative each other in a form named "Star Schema". The data in project DW is required some fine degree to form basic data, summarized data and specialized data. OLAP is applied to statistics analysis, data classification, comparable analysis and forecast analysis through processing multi-dimension data;the proper DM method is involved in cost estimate, market forecast, material optimum and risk assessing respectively. The project decision support system based project DW, DM and OLAP makes decision more efficient and promptly for every department and unit which participant in the project combining INTERNET techniques.The owner's project risk factors are concluded as: environment risk, project system risk, organization risk, constructor risk and supervisor risk, the paper qualify each risk factor to define project's risk rank, then select every possible risk decision ruler to fill the risk decision table which rough set theory is applied to and deduct unnecessary information to gainequivalent minimum risk table to support project risk decision efficiently for the owner.