A Closed Frag-Shells Cubing Algorithm on High Dimensional and Non-Hierarchical Data Sets

In view of high-dimensional and non-hierarchical large data sets, an improved CFSC (Closed Frag-Shells Cube) method is proposed based on the Frag-Shells method in this paper. When the Data Cube is generated, the high-dimensional data is divided into several low-dimensional data fragments by using the idea of partitioning cubes into dimension attributes. For each dimension data segment, the closed cubes of each dimension data segment are calculated using the closed cube calculation. A query bitmap is added to each fragment, and a query index table of closed segments is constructed by using bit map index technology to reduce the storage space occupied by the result set and to increase the query efficiency. Based on the application of multidimensional analysis of water conservancy census data, it is proved that this method can effectively reduce the storage space of cube data of water conservancy census data and improve the efficiency of OLAP (online analytical processing) query.