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Schema Evolution Models and Languages for Multidimensional Data Warehouses
Abstract
A Data Warehouse (DW) is a collection of historical data, built by gathering and integrating data from several sources, which supports decisionmaking processes (Inmon, 1992). On-Line Analytical Processing (OLAP) applications provide users with a multidimensional view of the DW and the tools to manipulate it (Codd, 1993). In this view, a DW is seen as a set of dimensions and cubes (Torlone, 2003). A dimension represents a business perspective under which data analysis is performed and organized in a hierarchy of levels that correspond to different ways to group its elements (e.g., the Time dimension is organized as a hierarchy involving days at the lower level and months and years at higher levels). A cube represents factual data on which the analysis is focused and associates measures (e.g., in a store chain, a measure is the quantity of products sold) with coordinates defined over a set of dimension levels (e.g., product, store, and day of sale). Interrogation is then aimed at aggregating measures at various levels. DWs are often implemented using multidimensional or relational DBMSs. Multidimensional systems directly support the multidimensional data model, while a relational implementation typically employs star schemas(or variations thereof), where a fact table containing the measures references a set of dimension tables.
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