Using data warehouses to analyse multidimensional data is a significant task in company decision-making.The data warehouse merging process is composed of two steps: matching multidimensional components and then merging them. Current approaches do not take all the particularities of multidimensional data warehouses into account, e.g., only merging schemata, but not instances; or not exploiting hierarchies nor fact tables. Thus, in this paper, we propose an automatic merging approach for star schema-modeled data warehouses that works at both the schema and instance levels. We also provide algorithms for merging hierarchies, dimensions and facts. Eventually, we implement our merging algorithms and validate them with the use of both synthetic and benchmark datasets.
翻译:利用数据仓库分析多层面数据是公司决策的一项重要任务。 数据仓库合并过程由两个步骤组成:匹配多层面组成部分,然后将其合并。目前的方法没有考虑到多层面数据仓库的所有特性,例如,只将系统组合,但并不考虑各种情况;或者不利用等级或事实表。因此,我们在本文件中提议对在系统图和实例两级工作的恒星模型模型数据仓库采取自动合并办法。我们还为合并等级、维度和事实提供算法。最终,我们采用合并算法,并同时使用合成和基准数据集来验证这些算法。