Study And Implementation On Key Techniques Of Dwarf Based Semantic OLAP

OLAP technology is very important in decision support system, which is used to aid knowledge worker' decision and analysis. In order to reduce the query response time, we ought to compute the CUBE in advance. The computation of data CUBE is necessary but high cost in data warehouse. It is becoming one of the research focuses to compute data CUBE efficiency in data warehouse.The computation of CUBE is the basis of OLAP query and analysis. The inherent difficulty with the cube operation is the size, both for computing and storing it. Some researchers have provided some useful cube computation including the use of parallelism, and mapping string dimension types to integers for reducing the storage. The problem is exacerbated by the fact that new applications include an increasing number of dimensions and, thus, the explosion on the size of the cube is a real problem. All methods proposed in some literature try to deal with the space problem. The storage will be reduced dramatically by compressing data CUBE while the computation time can be saved efficiently.Dwarf is a highly compressed structure with OLAP semantic held. Dwarf solves the storage space problem, by identifying prefix and suffix structural redundancies in the structure of the cube and factors them out by coalescing their store. Generally, prefix redundancy is high on dense areas of cubes but suffix redundancy is significantly higher for sparse areas. Putting the two together fuses the exponential sizes of high dimensional full cubes into a dramatically condensed data structure.This thesis studies the issues of compressing semantic cube based on Dwarf. An improved algorithm—Q-Dwarf is proposed to solve the question of I/O frequently. Moreover, we go deeper into several issues after implementing the basic Dwarf CUBE. Firstly, the auxiliary cluster algorithm is implemented in...