By studying the methods of data mining and OLAP operation, and the creation and materialization of data cube and by improving traditional DM algorithms, the algorithms of OLAP mining engine is put forward and implemented. 通过研究数据挖掘方法和OLAP操作的特点,以及数据立方的构建和物化,对传统的DM算法进行了改进,设计并实现了更能适应OLAP数据挖掘引擎的算法。
The materialization of spatial data cube is based on the traditional data cubes and is different from traditional materialization. 空间数据立方体的物化在传统数据立方体的基础上展开,又不同于传统数据立方体的物化。
The warehousing of Web data includes three consecutive stages: data extraction, data integration and data materialization. Web数据的数据仓库化包括数据抽取、数据集成和数据物化三个连续的阶段。
The pointer intersection algorithm is adapted by introduction ORACLE SPATIAL object-relational model and Microsoft data cube construction techniques to vector spatial measure materialization. 基于Oraclespatial对象关系模型和微软立方体构建技术提出了矢量型空间度量指针交集物化算法的改进算法,以解决矢量型视觉特征的物化问题。
The thesis mainly study some technologies about OLAP, including construction of data cubes 、 Selective materialization of traditional and spatial dada cubes. In the thesis we advance a way to construct data cube with code, and summarize a proper data structure. 论文随后针对OLAP技术中的立方体构建、传统数据立方体和空间数据立方体的选择性物化技术进行了研究,提出了一种基于编码的立方体构建方法,并总结了一种合适的数据立方体的数据结构。
In this paper, according to our research on data warehouse prototype of railway freight transportation, we discuss a composite lattice structure of multidimensional data cube and its materialization strategy. We introduce a greedy algorithm and a space boundary algorithm. 本文结合铁路货运数据仓库模型的研究,探讨了数据仓库数据立方体多维视图的依赖格组织法及其物化策略,介绍了物化视图选择的贪心法和空间边界法。
According to the discussion about that how to efficiently realize the cube computing in data warehouse, this paper presents the algorithms for implementing two kinds of materialization of the cubes. 通过对数据仓库中如何有效的进行数据立方体计算的讨论,提出了实现数据立方体部分物化和全物化的算法。