Logical Data Warehouse Design - Data Warehouse Systems: Design and Implementation - page 139

The third solution to the problem of slowly changing dimensions, called
type 3, consists in introducing an additional column for each attribute subject
to change, which will hold the new value of the attribute. In our case,
attributes CategoryName and Description changed since when product prod1
changes category from cat1 to cat2 , the associated description of the category
also changes from desc1 to desc2 . The following table illustrates this solution:

Product
Key

Product
Name

Discontinued Category
Name

New
Category Description

New
Description

p1 prod1 No cat1 cat2 desc1 desc2

p2 prod2 No cat1 Null desc1 Null

p3 prod3 No cat2 Null desc2 Null

p4 prod4 No cat2 Null desc2 Null

Note that only the two more recent versions of the attribute can be
represented in this solution and that the validity interval of the tuples is
not stored.
It is worth noticing that it is possible to apply the three solutions above,
or combinations of them, to the same dimension. For example, we may apply
a correction (type 1) together with tuple versioning (type 2) or with attribute
addition (type 3) for various attributes of a dimension table.
In addition to these three classic approaches to handle slowly changing
dimensions, more sophisticated (although more dicult to implement)
solutions have been proposed. We briefly comment on them next.
The type 4 approach aims at handling very large dimension tables and
attributes that change frequently. This situation can make the dimension
tables to grow to a point that even browsing the dimension can become very
slow. Thus, a new dimension, called a minidimension , is created to store the
most frequently changing attributes. For example, assume that in the Product
dimension there are attributes SalesRanking and PriceRange , which are likely
to change frequently, depending on the market conditions. Thus, we will
create a new dimension called ProductFeatures ,withkey ProductFeaturesKey ,
and the attributes SalesRanking and PriceRange , as follows:

Product
FeaturesKey

Sales
Ranking

Price
Range

pf1 1 1-100

pf2 2 1-100

··· ··· ···

pf200 7 500-600

As can be seen, there will be one row in the minidimension for each unique
combination of SalesRanking and PriceRange encountered in the data, not one
row per product.