Oriented Data Warehouse, Multi-table Joins And Aggregation Algorithm Research

Data warehouse and business intelligence is to provide information and tools that customized operational and strategic business decisions need for business personnel. The online analytical processing (OLAP) is one of the main applications of data warehouses. ROLAP is a kind of OLAP (Online Analytical Processing) and is most widely used. Its major function is to manage aggregate data which is needed in the decision-making. Aggregate data is generally involved in multi-table joining and aggregating the query data. Enhancing the performance of these operations becomes the key to improve operational response efficiency of OLAP. In this paper, ROLAP query technologies are studied for the huge amount of data. The main research works are as follows:(1) An improved aggregation algorithm IMuGA based on group number is presented. The algorithm takes full advantage of the time dimension table's particularity, directly get the group attribute values through the fact table, greatly reduce query times on time dimensional table in multi-table join and improve the efficiency of OLAP query. Experimental results show that the algorithm is effective.(2) An aggregation algorithm is presented by using hierarchical sequence dimensions based on the concepts of partial order and mapping. Using the aggregation relationship between dimensional attributes, the algorithm transforms multi-table joining into dimensional query ranges in the aggregation computing based on group number by restricting the order of elements in hierarchical chain,and improves the efficiency of multi-table join and aggregation. The experimental results validate the validity and the correctness of the algorithm.