Research On The Storage Technique Of Data Cube Based-on Dimension Hierarchy

Data cube is the kernel conception of data warehouse and on-line analytical processing (OLAP). It usually needs to pre-compute and save the data cube in disk in order to promptly answer complex multidimensional queries in the OLAP applications. But the large size brings about a lot of trouble when they are computed and saved. To decrease disk storage cost and improve queries performance are very important but contradictive goals of data cube research. For the sake of resolving these problems, it needs to explore the effective structures of data cube.A new approach named ICODH is improved here, which computes tuples in recursion from bottom to top. When one dimension is computed, it computes from the coarse granularity to the fine granularity in circle. By sharing the sorting costs, it decreases the reading and writing operations of the disk in order to reduce the dimension hierarchy cube's computation time. On the other side, basing on the research of dimension hierarchy encoding technique, this paper also proposes an effective method to encode the dimension table. This approach preserves the semantic relations by virtue of the compressing mechanism. Through two sides, the data cube speeds up the computation and improves the performance of query.Condensed Data Cube has been proposed as an effective approach for reducing data cube's size, but there are still lots of prefix-redundancy in the data cube, such as intra-cuboid prefix redundancies and among-cuboid prefix redundancies. For this, a data cube structure named IDHC is extended here. It combines two techniques—BST condensing and intra-cuboid prefix-sharing. According to the character of dimension hierarchy, it clusters the cube tuples which has the same grouping dimension set (or the single dimension set), reduces the size of cube because the tuples in same cluster can share the prefix. Meantime, when these tuples are preserved in disk, the algorithm which tuples generated inthe same batch is proposed in order to eliminate tuple comparisons. Thisapproach eliminates comparisons among tuples in cuboid which containsonly one grouping dimension, and computes IDHC in batch mode wasproposed.