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4.2 Data Warehouse Modeling: Data Cube and OLAP 139

all 0-D (apex) cuboid

item location 1-D cuboids
Item
location
time supplier
time, supplier item, supplier
time, supplier
item, supplier
time, item location, 2-D cuboids
time, location item, location supplier
time, location
item, location
time, location, supplier 3-D cuboids
time, location, supplier
time, item, location
time, item, supplier
item, location,
supplier
time, item, location, supplier 4-D (base) cuboid

Figure 4.5 Lattice of cuboids, making up a 4-D data cube for time, item, location, and supplier. Each
cuboid represents a different degree of summarization.

4.2.2 Stars, Snowflakes, and Fact Constellations: Schemas for
Multidimensional Data Models

The entity-relationship data model is commonly used in the design of relational
databases, where a database schema consists of a set of entities and the relationships
between them. Such a data model is appropriate for online transaction processing.
A data warehouse, however, requires a concise, subject-oriented schema that facilitates
online data analysis.
The most popular data model for a data warehouse is a multidimensional model,
which can exist in the form of a star schema, a snowflake schema, or a fact constellation
schema. Let’s look at each of these.

Star schema: The most common modeling paradigm is the star schema, in which the
data warehouse contains (1) a large central table (fact table) containing the bulk of
the data, with no redundancy, and (2) a set of smaller attendant tables (dimension
tables), one for each dimension. The schema graph resembles a starburst, with the
dimension tables displayed in a radial pattern around the central fact table.

Example 4.1 Star schema. A star schema for AllElectronics sales is shown in Figure 4.6. Sales are con-
sidered along four dimensions: time, item, branch, and location. The schema contains
a central fact table for sales that contains keys to each of the four dimensions, along