| Since the recent years, data warehouse has become one of the most popular research areas of computer technologies. Data warehouse and online analytical processing are applied extensively in finance, insurance, government and many other industries to make decisions for customer relationship managements, enterprise resource projects, etc. Because analyses are oriented to a mass of data in data warehouse, efficiency is an essential problem. Multi-table joining is usually involved in OLAP (online analytical processing) queries, so how to improve its performance becomes a key issue of OLAP query processing.It is discussed in the paper that how to improve the efficiency of multi-table joining query.Firstly, we introduce the concept of data warehouse and online analytical processing along with optimization technologies of data warehouse.Secondly, we give an introduction to SQL and multi-table joining algorithms and analyze the advantages and disadvantages of join index and multi-table joining algorithms in improving the efficiency of multi-table joining query.Thirdly, the method of distortional multi-table joining index is proposed, which can achieve better performance on the aspect of multi-table joining than parallel multi-joining algorithm and joining index. Based on Query Model-Base, in which queries are expressed in SQL, the algorithm builds a series of distortional multi-table joining fact tables and indexes of them. In multi-table joining queries, distortional multi-table joining fact tables join with dimension tables instead of fact tables, and further more, distortional multi-join indexes can be dynamically updated in query processing. Theoretical analysis and experimental results show that distortional multi-table joining index is an efficient method for multi-table joining in data warehouse.The method is compatible with other methods which are used to improve the efficiency of multi-table joining query. In addition, it would be more effective if use them simultaneously. |
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