Cascaded Star: A Hyper-Dimensional Model for a Data Warehouse

  • Songmei Yu
  • Vijayalakshmi Atluri
  • Nabil Adam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


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.


Relational Algebra Complex Query Dimension Table Star Model Single Star 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Songmei Yu
    • 1
  • Vijayalakshmi Atluri
    • 1
  • Nabil Adam
    • 1
  1. 1.MSIS Department and CIMICRutgers UniversityUSA

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