Databases Reference
In-Depth Information
User control versus automation
: Online analytical processing in data warehouses
is a user-controlled process. The selection of dimensions and the application of
OLAP operations (e.g., drill-down, roll-up, slicing, and dicing) are primarily directed
and controlled by users. Although the control in most OLAP systems is quite user-
friendly, users do require a good understanding of the role of each dimension.
Furthermore, in order to find a satisfactory description of the data, users may need to
specify a long sequence of OLAP operations. It is often desirable to have a more auto-
mated process that helps users determine which dimensions (or attributes) should
be included in the analysis, and the degree to which the given data set should be
generalized in order to produce an interesting summarization of the data.
This section presents an alternative method for concept description, called
attribute-
oriented induction
, which works for complex data types and relies on a data-driven
generalization process.
4.5.1
Attribute-Oriented Induction for Data Characterization
The
attribute-oriented induction (AOI)
approach to concept description was first pro-
posed in 1989, a few years before the introduction of the data cube approach. The data
cube approach is essentially based on
materialized views
of the data, which typically
have been precomputed in a data warehouse. In general, it performs offline aggre-
gation before an OLAP or data mining query is submitted for processing. On the
other hand, the attribute-oriented induction approach is basically a
query-oriented
,
generalization-based, online data analysis technique. Note that there is no inherent
barrier distinguishing the two approaches based on online aggregation versus offline
precomputation. Some aggregations in the data cube can be computed online, while
offline precomputation of multidimensional space can speed up attribute-oriented
induction as well.
The general idea of attribute-oriented induction is to first collect the task-relevant
data using a database query and then perform generalization based on the examination
of the number of each attribute's distinct values in the relevant data set. The generali-
zation is performed by either
attribute removal
or
attribute generalization
. Aggregation
is performed by merging identical generalized tuples and accumulating their respec-
tive counts. This reduces the size of the generalized data set. The resulting generalized
relation can be mapped into different forms (e.g., charts or rules) for presentation to
the user.
The following illustrates the process of attribute-oriented induction. We first discuss
its use for characterization. The method is extended for the mining of class comparisons
in Section 4.5.3.
Example 4.11
A data mining query for characterization.
Suppose that a user wants to describe
the general characteristics of graduate students in the
Big University
database, given
the attributes
name, gender, major, birth
place, birth date, residence, phone# (telephone
