Data in a warehouse typically has multiple dimensions of interest, such as location, time, and product. It is well-recognized that these dimensions have hierarchies defined on them, such as "store-city-state-region" for location. The standard way to model such data is with a star/snowflake schema. However, current approaches do not give a first-class status to dimensions. Consequently, a substantial class of interesting queries involving dimension hierarchies and their interaction with the fact tables are quite verbose to write, hard to read, and difficult to optimize.
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