In data warehousing, the data cubes are n-dimensional. Let us suppose that we would like to view our sales data with an additional fourth dimension, such as a supplier. The 3-D data of the table are represented as a series of 2-D tables.Ĭonceptually, we may represent the same data in the form of 3-D data cubes, as shown in fig: The measured display in dollars sold (in thousands). For example, suppose we would like to view the data according to time, item as well as the location for the cities Chicago, New York, Toronto, and Vancouver. Let suppose we would like to view the sales data with a third dimension. Facts are generally quantities, which are used for analyzing the relationship between dimensions.Įxample: In the 2-D representation, we will look at the All Electronics sales data for items sold per quarter in the city of Vancouver. Thus, the fact table contains measure (such as Rs_sold) and keys to each of the related dimensional tables.ĭimensions are a fact that defines a data cube. A multidimensional data model is organized around a central theme, like sales and transactions. Data cubes usually model n-dimensional data.Ī data cube enables data to be modeled and viewed in multiple dimensions. OLAP tools are based on the multidimensional data model. The model view data in the form of a data cube. If a query contains constants at even lower levels than those provided in a data cube, it is not clear how to make the best use of the precomputed results stored in the data cube. Techniques should be developed to handle sparse cubes efficiently. Data cubes could be sparse in many cases because not every cell in each dimension may have corresponding data in the database. For example, a dimension table for items may contain the attributes item_name, brand, and type.ĭata cube method is an interesting technique with many applications. Each dimension may have a table identify with it, known as a dimensional table, which describes the dimensions. These dimensions enable the store to keep track of things like monthly sales of items, and the branches and locations at which the items were sold. The measure attributes are aggregated according to the dimensions.įor example, XYZ may create a sales data warehouse to keep records of the store's sales for the dimensions time, item, branch, and location. Another attributes are selected as dimensions or functional attributes. ![]() Specific attributes are chosen to be measure attributes, i.e., the attributes whose values are of interest. The general idea of this approach is to materialize certain expensive computations that are frequently inquired.įor example, a relation with the schema sales (part, supplier, customer, and sale-price) can be materialized into a set of eight views as shown in fig, where psc indicates a view consisting of aggregate function value (such as total-sales) computed by grouping three attributes part, supplier, and customer, p indicates a view composed of the corresponding aggregate function values calculated by grouping part alone, etc.Ī data cube is created from a subset of attributes in the database. The data cube method has a few alternative names or a few variants, such as "Multidimensional databases," "materialized views," and "OLAP (On-Line Analytical Processing)." When data is grouped or combined in multidimensional matrices called Data Cubes.
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