Research On The Compression Method And CUBE Computation Of Multidimensional Data In Datawarehouse

The traditional databases are the main information sources of datawarehouses; data warehouses provide an integrated data environment for Online Analytical Processing (OLAP), Decision Support System (DSS) and Data Mining (DM). Organizing and managing the data efficiently is one of the keys of implementing data warehouses. This thesis studies it deeply on the aspects of data warehouses' concept model and OLAP implementation.The store of multidimensional data and data operation algorithms are a basic aspect in the research field of data warehouses.Summarizing and analysing the basic concept,the designing procedure of data warehouse, a data warehouses' multidimensional data model, show the requirement of storage and aggregation optimizing methods of multidimensional data.It also establishes theoretical foundation for the storage and aggregation optimizing methods of multidimensional data.The logic organization mode of multidimensional data is one of the keys of OLAP implementation, this thesis summarizes the two organizing ways of multidimensional data-relational mode and array mode thoroughly, and places emphases on the researches of array mode, including the storage structure of multidimensional data, the construction methods of multidimensional arrays, the compressing methods of sparse arrays, the principles of dividing arrays into chunks and the access methods of chunk arrays, and also this thesis realizes a storage instance of array mode based on the above theoretical analyses.One means of improving the performance of OLAP is to compute multidimensional aggregations efficiently. This thesis summarizes the main optimizing methods of computing aggregations, on which the correlative concepts are formally defined, furthermore, this thesis emphasizes the research of optimizing methods of array mode and proposes an aggregation algorithm.It makes use of optimizing methods including Small-parent, amortize-scans and Cache-results,and add support of the inner level of the dimension of cube query. Using the effective data compression method,it divides array into parts and computes each separately. after all parts have been accomplished, it merges the intermediate results into integrated aggregations. The analysis shows that this algorithm can make the best use of memory and reduce I/O times.At the end of this thesis, the researches are summarized and the future work is presented.