The Research Of Data Cube Incremental Calculation Method In OLAP

Data cube is the core of data warehouse and the OLAP. When the real-time update of data in data warehouse, the corresponding Data cube has to be updated, or OLAP queries will be out of date data. Data cube to update most of the incremental maintenance, namely the amount of change on the basis of the base table refresh existing data cube. How to reduce the data cube take up disk space and speed up the calculation of data cube has become a hot issue. Under such a background, this paper carried out the OLAP data cube in incremental updating algorithm research, the main work is as follows:1. This paper first introduces the basic knowledge of OLAP, the definition and calculation of data cube and queries, and then introduces several classic data cube structure and analyzes the advantages and disadvantages of various cube. Then introduce cube knowledge aggregation, it analyzes typical aggregation algorithm Pipesort, Pipehash algorithm,,Multiway algorithm and BUC algorithm, etc. at the same time gives a optimization method of aggregation.2. The research is mainly focused on the extensible data organization model of multidimensional array as a data cube, and by removing redundant logic completely compression method and physical compression method based on HOEA on scalable multi-dimensional array twice compression effectively, greatly reduce the space complexity of the data cube. Proposed the EMA Cube (Extendible Multidimensional Array Data Cube) extended Multidimensional Array Data Cube model. Through examples verify that the model eliminates the redundant data unit completely, effective compression of data is stored.3. The analysis there are two kinds of data cube update implementation strategy, namely, incremental updating and recalculate. The EMA-Cube data Cube incremental updating calculation method, assisted by sharing d information table (including the historical value table, the first address table and the coefficient to scale) complete Cube update, effectively reduces the data Cube computation time and update the maintenance time.