Section 3 presents a Z specification of a data warehouse
star schema and illustrates the utility of discharging a proof obligation arising from the specification.
* UPDATING FACT ROWS inside a
star schema set of tables is never a best practice.
Indexing OLAP data and joining indices was as follows Join index: JI(R-id, S-id) where R (R-id, ...) S (Sid, ...), traditional indices map and the values to a list of record ids, it materializes relational join in JI file and speeds up relational joining rather costly operation in data warehouses, join index relates the values of the dimensions of a
star schema to rows in the fact table illustrated from fact table Sales and two dimensions city and product.
The most natural way to model a data warehouse is as a
star schema, which is the simplest DW model.
Multi-Dimensional Entity Relationship (ME/R) Model: The data warehouse Reverse Engineering (RE) provides the ME/R model or
star schema. It provides schema in XML format which can be explored for keyword matching further.
In this case, the fact table will not have only one level of dimensions tables so this is why snowflake schema is better suited than the
star schema for the modelling (Adamson, 2010).
This guide to best practices in dimensional design provides information on
star schema design techniques for data warehouses.
Taking the relationship type Cont ribut ion in Figure 7 as core type of a
star schema, the subschema containing Contribution, Member, Document, Project, and Time defines another star structure.
A
star schema requires that a dimension hierarchy is contained with in a single table.
(a) Transforming to
Star Schema: The individual historical data is transformed into
star schema.