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Multidimensional normal forms for data warehouse design
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AI
DOI：10.1016/S0306-4379(02)00024-8.png)
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A data warehouse is an integrated and time-varying collection of data derived from operational data and primarily used in strategic decision making by means of OLAP techniques. Although it is generally agreed that warehouse design is a non-trivial problem and that multidimensional data models as well as star or snowflake schemata are relevant in this context, there exist neither methods for deriving such a schema from an operational database nor measures for evaluating a warehouse schema. In this paper, a sequence of multidimensional normal forms is established that allow reasoning about the quality of conceptual data warehouse schemata in a rigorous manner. These normal forms address traditional database design objectives such as faithfulness, completeness, and freedom of redundancies as well as the notion of summarizability, which is specific to multidimensional database schemata. (C) 2002 Elsevier Science Ltd. All rights reserved.
Keyword:
data warehouse design
multidimensional data
normal form
summarizability
functional dependency
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3.9
论文数:
2.8K
被引数:
1.8K
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