概念集成建模框架：从多维模型中抽象
The Conceptual Integration Modeling Framework: Abstracting from the Multidimensional Model
摘要：
数据仓库绝大多数是通过自底向上的过程构建的，该过程从源的标识开始，继续从这些源提取和转换数据，然后根据期望的多维关系模式将数据加载到一组数据集市中。最终用户商业智能工具被添加到具体化的多维模式之上，以推动组织中的决策制定。不幸的是，这种自下而上的方法在所需的熟练用户和仓库的庞大规模方面都是昂贵的。本文提出了一个自顶向下的框架，其中数据仓库是由一个概念模型驱动。该框架提供了设计时和运行时间环境。在设计时，业务用户首先使用概念建模语言作为多维对象模型来指定所需的业务信息;然后将概念模型映射到预先存在的逻辑多维表示。在运行时间，系统将把用户概念模型与映射一起变换成逻辑多维表示上的视图。我们专注于用户如何从现有的数据仓库中抽象概念，以及如何将这个概念模型映射到逻辑多维表示。我们还给出了一个指示，什么查询语言是使用的概念模型。最后，我们认为，我们的框架是一个步骤，沿着允许自动生成的数据仓库。
英文摘要：
Data warehouses are overwhelmingly built through a bottom-up process, which starts with the identification of sources, continues with the extraction and transformation of data from these sources, and then loads the data into a set of data marts according to desired multidimensional relational schemas. End user business intelligence tools are added on top of the materialized multidimensional schemas to drive decision making in an organization. Unfortunately, this bottom-up approach is costly both in terms of the skilled users needed and the sheer size of the warehouses. This paper proposes a top-down framework in which data warehousing is driven by a conceptual model. The framework offers both design time and run time environments. At design time, a business user first uses the conceptual modeling language as a multidimensional object model to specify what business information is needed; then she maps the conceptual model to a pre-existing logical multidimensional representation. At run time, a system will transform the user conceptual model together with the mappings into views over the logical multidimensional representation. We focus on how the user can conceptually abstract from an existing data warehouse, and on how this conceptual model can be mapped to the logical multidimensional representation. We also give an indication of what query language is used over the conceptual model. Finally, we argue that our framework is a step along the way to allowing automatic generation of the data warehouse.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
