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首页> 外文会议>Database and Expert Systems Applications; Lecture Notes in Computer Science; 4080 >Cascaded Star: A Hyper-Dimensional Model for a Data Warehouse
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Cascaded Star: A Hyper-Dimensional Model for a Data Warehouse

机译：级联星：数据仓库的超维模型

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摘要

A data warehouse is defined as subject-oriented, integrated, time-variant and nonvolatile collection of data. Often, the data representing different subjects is multi-dimensional in nature, where each dimension of each subject could again be multi-dimensional. We refer to this as hyper-dimensional nature of data. Traditional multi-dimensional data models (e.g., the star schema) cannot adequately model these data. This is because, a star schema models one single multi-dimensional subject, hence a complex query crossing different subjects at different dimensional levels has to be specified as multiple queries and the results of each query must be composed together manually. In this paper, we present a novel data model, called the cascaded star model, to model hyper-dimensional data, and propose the cascaded OLAP (COLAP) operations that enable ad-hoc specification of queries that encompass multiple stars. Specifically, our COALP operations include cascaded-roll-up, cascaded-drill-down, cascaded-slice, cascaded-dice and MCUBE. We show that COLAP can be represented by the relational algebra to demonstrate that the cascaded star can be built on top of the traditional star schema framework.
机译：数据仓库被定义为面向主题的，集成的，随时间变化且非易失的数据收集。通常，代表不同主题的数据本质上是多维的，其中每个主题的每个维度又可以是多维的。我们将其称为数据的超维性质。传统的多维数据模型（例如，星型模式）无法对这些数据进行适当的建模。这是因为星型模式为一个多维主题建模，因此必须将跨越不同维度级别不同主题的复杂查询指定为多个查询，并且每个查询的结果必须手动组合在一起。在本文中，我们提出了一种称为级联星型模型的新型数据模型，以对超维数据进行建模，并提出了级联OLAP（COLAP）操作，该操作可对包含多个星型的查询进行即席规范。具体来说，我们的COALP操作包括级联上滚，级联下钻，级联切片，级联骰子和MCUBE。我们证明了COLAP可以由关系代数表示，以证明级联星可以建立在传统星图框架之上。

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