Attribute Discretization: Using the "Equal Areas" Method - Page 2

Using the Equal Areas Attribute Discretization Method

As we learned in Introduction to Attribute Discretization, whenever we work with attributes, we can expect to encounter two general types of values, discrete and contiguous. Discrete values stand apart distinctly, and have clearly defined logical “boundaries” between themselves. Citing the Gender attribute, within the Customer dimension of the Adventure Works sample UDM, wherein the attribute is considered to have only one of two discrete values, female or male, we noted that possible values are naturally discrete for the lion’s share of attributes occurring in the business world.

In contrast to discrete values, we noted that contiguous values do not stand apart distinctly, but flow along, as if in a continuous line. Moreover, we discussed the fact that contiguous values, especially within large populations, can have very large numbers of possible values, and that information consumers can find it difficult to work effectively and efficiently within such wide ranges of values. As an example, we cited the Vacation Hours attribute, within the Employee dimension of the Adventure Works sample UDM, an attribute which could have a wide range of possible values, depending upon how many employees are involved, whether there are limits on how many vacation days they can accumulate, and considerations of this nature. (The member values are based directly upon the values contained within the VacationHours column of the DimEmployee table - with many of the values shared among multiple employees.) The sheer number of values might make working with them cumbersome for information consumers, if they are simply made available in their existing state.

As we discussed in Introduction to Attribute Discretization, discretization can help us to make it easier for information consumers to work with large numbers of possible attribute member values. As discretization creates a manageable number of groups of attribute values that are clearly separated by boundaries, we can thereby group contiguous values into sets of discrete values, via a system-generated collection of consecutive dimension members known as member groups.

We also discussed, in Introduction to Attribute Discretization, that once the discretization process groups the attribute members into the member groups, the member groups are then housed within a level within the dimensional hierarchy. (A given level within a dimensional hierarchy can contain either members or member groups, but not both.) When information consumers browse a level that contains member groups, they see the names and cell values of the member groups. The members generated by Analysis Services to support member groups are called grouping members, and they look like ordinary members.

Analysis Services affords us flexibility in methods of attribute discretization, based upon algorithms of varying complexity. The different methods of discretization all have the same function – to group contiguous values into sets of discrete values. (Analysis Services can also effectively discretize large numbers of discrete values, such as Social Security numbers.)

They simply manage grouping via different approaches. Beyond the Dimension Designer, Analysis Services also supports user-defined discretization, via data definition language (DDL), should the “out – of – the – box” approaches not meet the business needs of our local environments. Moreover, we can alternatively implement custom discretization via the underlying data warehouse, using views at the relational level; named calculations in the data source view; calculated members in Analysis Services; or via other approaches.

Analysis Services supports four DiscretizationMethod property settings, which include three pre-defined discretization methods:

• None (default): Analysis Services performs no grouping, and simply displays the attribute members.
• Automatic: Analysis Services selects the method that best represents the data: either the EqualAreas method or the Clusters method.
• EqualAreas: Analysis Services attempts to divide the members in the attribute into groups that contain an equal number of members.
• Clusters: Analysis Services groups members by performing single-dimensional clustering on the input values by using the K-Means algorithm. It uses Gaussian distributions. This can only be used for numeric columns.

The DiscretizationMethod property determines whether Analysis Services is to create groupings, and then determines the type of grouping that is performed. As we learned in Introduction to Attribute Discretization, Analysis Services does not perform any groupings by default (the default setting for the DiscretizationMethod property is “None”). When we enable Equal Areas grouping, as we shall see in the practice session that follows, we direct Analysis Services (via the DiscretizationMethod property) to create group ranges so that the total population of dimension members is distributed equally across the groups. We then process the affected dimension / cube and Analysis Services creates group ranges, and then distributes the total population of attribute members appropriately across those groups. As we shall see, once we specify a grouping method, we next specify the number of groups, by using the DiscretizationBucketCount property (its default value is zero).

As we have noted throughout my MSSQL Server Analysis Services column, as well as throughout my other Database Journal series’, one of the most important objectives in building a high performance Analysis Services solution is an efficient and effective dimension design. The identification of opportunities where we can effectively use attribute discretization, and the effective design and placement of the member groups that we generate thereby, can mean the provision of a much more consumer-friendly interface for our clients and employers. We will gain hands - on exposure to the Equal Areas method of attribute discretization in the practice session that follows. Before we get started working within a sample cube clone, we will need to prepare the local environment for the practice session. We will take steps to accomplish this within the section that follows.

Preparation: Locate and Open the Sample Basic UDM Created Earlier

In Dimensional Model Components: Dimensions Part I, we created a sample basic Analysis Services database within which to perform the steps of the practice sessions we set out to undertake in the various articles of this subseries. Once we had ascertained that the new practice database appeared to be in place, and once we had renamed it to ANSYS065_Basic AS DB, we began our examination of dimension properties. We next undertook an examination of attributes within the same practice environment, which we will now access (as we did within the earlier articles of this subseries)) by taking the following steps within the SQL Server Business Intelligence Development Studio.

The Connect to Database dialog appears.

SQL Server Business Intelligence Development Studio briefly reads the database from the Analysis Server, and then we see the Solution Explorer populated with the database objects. Having overviewed attribute discretization in this and the previous article, we will now get some hands-on exposure to the use of Equal Areas attribute discretization for the members of a representative dimension attribute within our practice UDM.