Junk Dimensions(原创)

Junk Dimension

A junk dimension is a convenient grouping of typically low-cardinality flags and indicators. By creating an abstract dimension, these flags and indicators are removed from the fact table while placing them into a useful dimensional framework.A Junk Dimension is a dimension table consisting of attributes that do not belong in the fact table or in any of the existing dimension tables. The nature of these attributes is usually text or various flags, e.g. non-generic comments or just simple yes/no or true/false indicators. These kinds of attributes are typically remaining when all the obvious dimensions in the business process have been identified and thus the designer is faced with the challenge of where to put these attributes that do not belong in the other dimensions.

One solution is to create a new dimension for each of the remaining attributes, but due to their nature, it could be necessary to create a vast number of new dimensions resulting in a fact table with a very large number of foreign keys. The designer could also decide to leave the remaining attributes in the fact table but this could make the row length of the table unnecessarily large if, for example, the attributes is a long text string.

The solution to this challenge is to identify all the attributes and then put them into one or several Junk Dimensions. One Junk Dimension can hold several true/false or yes/no indicators that have no correlation with each other, so it would be convenient to convert the indicators into a more describing attribute. An example would be an indicator about whether a package had arrived, instead of indicating this as “yes” or “no”, it would be converted into “arrived” or “pending” in the junk dimension. The designer can choose to build the dimension table so it ends up holding all the indicators occurring with every other indicator so that all combinations are covered. This sets up a fixed size for the table itself which would be 2^x rows, where x is the number of indicators. This solution is appropriate in situations where the designer would expect to encounter a lot of different combinations and where the possible combinations are limited to an acceptable level. In a situation where the number of indicators are large, thus creating a very big table or where the designer only expect to encounter a few of the possible combinations, it would be more appropriate to build each row in the junk dimension as new combinations are encountered. To limit the size of the tables, multiple junk dimensions might be appropriate in other situations depending on the correlation between various indicators.

Junk dimensions are also appropriate for placing attributes like non-generic comments from the fact table. Such attributes might consist of data from an optional comment field when a customer places an order and as a result will probably be blank in many cases. Therefore the junk dimension should contain a single row representing the blanks as a surrogate key that will be used in the fact table for every row returned with a blank comment field

Instance

In a junk dimension, we combine these indicator fields into a single dimension. This way, we'll only need to build a single dimension table, and the number of fields in the fact table, as well as the size of the fact table, can be decreased. The content in the junk dimension table is the combination of all possible values of the individual indicator fields.

Let's look at an example. Assuming that we have the following fact table:

FACT_TABLE

In this example, TXN_CODE, COUPON_IND, and PREPAY_IND are all indicator fields. In this existing format, each one of them is a dimension. Using the junk dimension principle, we can combine them into a single junk dimension, resulting in the following fact table:

FACT_TABLE

Note that now the number of dimensions in the fact table went from 7 to 5.

The content of the junk dimension table would look like the following:

In this case, we have 3 possible values for the TXN_CODE field, 2 possible values for the COUPON_IND field, and 2 possible values for the PREPAY_IND field. This results in a total of 3 x 2 x 2 = 12 rows for the junk dimension table.

By using a junk dimension to replace the 3 indicator fields, we have decreased the number of dimensions by 2 and also decreased the number of fields in the fact table by 2. This will result in a data warehousing environment that offer better performance as well as being easier to manage.

参考至：《Star Schema The Complete Reference》

http://www.1keydata.com/datawarehousing/junk-dimension.html

http://en.wikipedia.org/wiki/Dimension_%28data_warehouse%29

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