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Abstract

The paradigmatic view of data in typical decision support applications divides the attributes (or fields) in the data records into two groups: dimensional attributes and value attributes. The dimensional attributes classify the record, while the value attributes indicate a measured quantity. The dimensional attributes can be partitioned into a set of dimensions, which are orthogonal descriptions of the record. The attributes within a dimension form hierarchies of descriptions of the record, ranging from a coarse to a description. For example, the database might consist of records of retail sales collected from individual stores and brought together into a central data warehouse. This database might have three dimensions: store location, product, and time of sale. The value attribute might be the dollar value of the sale. A dimension might contain several attributes. For example, the store location dimension might consist of country, region, state, county, and zip code. These attributes form a hierarchy because knowing the value of a fine attribute (e.g., zip code) tells you the value of a coarse attribute (e.g., country). The attributes in the time dimension might be year, month, week, day, and hour. This dimension has multiple hierarchies because months do not contain an integral number of weeks. A large class of decision support queries ask for the aggregate value of one or more value attribute, where the aggregation ranges over all records whose dimensional attributes satisfy a selection predicate. For example, a query might be to find the sum of all sales of blue polo shirts in Palm Beach during the last quarter. A data table that can be described in terms of dimensions and value attributes is often called a 'data cube.' The records in our retail sales example can be imagined to exist in a three dimensional cube, the dimensions being the dimensional attributes. Queries, such as the example query, can be thought of as corresponding to sums over regions of the data cube. We describe herein a file structure (i.e., the Cube Forest) for storing a data cube that ensures fast response to the queries. The algorithms included herein are: (1) algorithms to load data into a cube forest; (2) algorithms to obtain an aggregate from the cube forest in response to a query; and (3) algorithms that compute an optimal cube forest structure.

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First Claim