OLAP Algorithm Research Based On Dimension Hierarchy For Data Cube

By analyzing OLAP algorithm based on dimension hierarchical for Data Cube of foreign and domain, we aware that there are many problems in the previous algorithms. The query and aggregate cost of simple join and aggregate algorithms is very large without adopting reasonable optimized method. New index structures adopt multi-table join and aggregate technique have been presented, but these methods are lack of considering of semantic characteristic. In the matter of Data Cube storage and incremental update, how to reduce the storage requirement and improve the efficiency of inquiries is a key problem in the database application. The paper has mainly focused on how to research OLAP algorithm based on dimension hierarchical for Data Cube. The solving of these problems has important meaning for e-commerce, Business Intelligence, Market decision-making and so on.Firstly, a join and aggregate algorithm based on dimension hierarchy (JABDH) is proposed in this paper. Considering the semantic characteristic which is not in all the dimension hierarchies, dimension hierarchical encoding is used to retrieve the matching dimension hierarchies and evaluate the set of query ranges for semantic dimension hierarchies. To improve the efficiency of multi-table join and aggregate operations for non-semantic dimensional hierarchies, join and aggregate operations is translated into bitmapped join index of fact table.Secondly, an improved storage algorithm for multidimensional Data Cube (ISMDC) is proposed in this paper. Dimensions are divided into association dimensions and no-association dimensions. The conception of Association Tree Cube is brought forward. On the no-association dimension, hierarchical B+ tree is used to remove redundancy and to form dimension hierarchical encoding. On the association dimension, encoding of the no-association dimension which is composed of dimension hierarchical encodings, is used for indexing. Thus, the value of aggregation will be searched out effectively and the efficiency of data cube pattern update will be increased.We implement the above algorithms, all of our experiments are performed on the real life datasets. The experimental results show the feasibility and effectiveness of our algorithms.