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<p><b>Abstract</b>—A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "<it>whether a total sales amount <b>1,000</b> items indicates a <b>good</b> or <b>bad</b> sales performance</it>" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of <it>quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization</it>, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses.</p>
Data warehouse, semantic model, algebraic operator, extended SQL, fuzzy set, membership function.

T. S. Dillon and L. Feng, "Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses," in IEEE Transactions on Knowledge & Data Engineering, vol. 15, no. , pp. 86-102, 2003.
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