2006 | OriginalPaper | Buchkapitel
Cascaded Star: A Hyper-Dimensional Model for a Data Warehouse
verfasst von : Songmei Yu, Vijayalakshmi Atluri, Nabil Adam
Erschienen in: Database and Expert Systems Applications
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.