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I Supplier. The supplier who manufactures the particular product sold. The company might distribute products from 50 different suppliers. I Customer. The customer who purchased the products. The company has several hundred customers. Some of the larger customers purchase products centrally and are serviced by a single salesperson; others purchase on a local basis and are served by local salespeople. I Region. The region of the country where the products were sold. Some of the company s customers operate in only one region of the country; others operate in two or more regions. I Month. The month when the products were sold. For comparison purposes, the company has decided to maintain 36 months (three years) of historical sales data in the warehouse. With these characteristics, each of the five dimensions is relatively independent of the others. Sales to a particular customer may be concentrated in a single region or in multiple regions. A specific category of product may be supplied by one or many different suppliers. The fact in each cell of the five-dimensional cube is the sales amount for that particular combination of dimension values. With the attributes just described, the fact cube contains over 35 million cells (24 categories 50 suppliers 300 customers 3 regions 36 months).

Float An approximate numeric type implemented with precision greater than or equal to the speci ed precision. The precision speci cation is optional. On supported platforms, FLOAT is usually implemented using oating-point numbers. The SQL syntax is

In most data warehouses, the most effective way to model the N-dimensional fact cube is with a star schema. A star schema for the distributor warehouse in the previous example is shown in Figure 21-3. Each dimension of the cube is represented by a dimension table. There are five of them in the figure, named CATEGORIES, SUPPLIERS, CUSTOMERS, REGIONS, and MONTHS. There is one row in each dimension table for each possible value of that dimension. The MONTHS table has 36 rows, one for each month of sales history being stored. Three regions produce a three-row REGIONS table. Dimension tables in a star schema often contain columns with descriptive text information or other attributes associated with that dimension (such as the name of the buyer for a customer, or the customer s address and phone number, or the purchasing terms for a supplier). These columns may be displayed in reports generated from the database. A dimension table always has a primary key that contains the value of the dimension. If the values of a dimension are numbers (such as a clothing size) or short text strings (such as a city name), the primary key may be this dimension value itself. It s more common for dimension values to be expressed in some type of code value. Three-letter airport codes and customer numbers are typical examples. In the sample warehouse of Figure 21-3, we assume that actual values are used as primary keys for REGIONS (East, West, and so on), CATEGORIES (clothing, shoes, and so on), and MONTHS. The other two dimensions use coded values (CUST_CODE for CUSTOMERS, SUPP_CODE for SUPPLIERS).

21:

Figure 21-3.

The largest table in the database is the fact table in the center of the schema. This table is named SALES in Figure 21-3. The fact table contains a column with the data values that appear in the cells of the N-cube in Figure 21-2. In addition, the fact table contains a column (or columns) that forms a foreign key for each of the dimension tables. In this example, there are five such foreign key columns. With this structure, each row represents the data for one cell of the N-cube. The foreign keys link the row to the corresponding dimension table rows for its position in the cube. The fact table typically contains only a few columns, but many rows hundreds of thousands or even millions of rows are not unusual in a production data warehouse. The fact column almost always contains numeric values, such as currency amounts, units shipped or received, or pounds processed. Virtually all reports from the warehouse involve summary data totals, averages, high or low values, percentages based on arithmetic computations on this numeric value. The schema structure of Figure 21-3 is called a star schema for obvious reasons. The fact table is at the center of a star of data relationships. The dimension tables form the points of the star. The relationships created by the foreign keys in the fact table connect the center to the points. With the star-schema structure, most business analysis questions turn into queries that join the central fact table with one or more dimension tables. Here are some examples: Show the total sales for clothing in January, by region.

Real An approximate numeric type with an implementation-de ned precision. You need to check the DBMS documentation for details. The SQL syntax is

SELECT FROM WHERE AND AND ORDER SALES_AMOUNT, REGION SALES, REGIONS MONTH = 01/1999 PROD_TYPE = "Clothing" SALES.REGION = REGIONS.REGION BY REGION