effective application development. From the practitioner’s point of view questions like
the following often arise:
• Is running database queries against large tables considered data mining?
• Can data mining and OLAP be considered synonymous?
• Is OLAP simply a way to speed up certain SQL queries?
The issue is being complicated even further by the fact that data analysis tools are often
implemented in terms of data retrieval functionality. Consider the data mining models in
the Microsoft SQL server which are implemented through extensions to the SQL
database query language (e.g. predict join) (Seidman, 2001) or the proposed SQL
extensions to enable decision tree classifiers (Sattler & Dunemann, 2001). OLAP cube
definition is routinely accomplished via the data definition language (DDL) facilities of
SQL by specifying either a star or snowflake schema (Kimball, 1996).
MAIN THRUST OF THE CHAPTER
The following sections contain the pair wise comparisons between the tools and
components considered in this chapter.
Database Queries vs. Data Mining
Virtually all modern, commercial database systems are based on the relational
model formalized by Codd in the 60s and 70s (Codd, 1970) and the SQL language (Date,
2000) which allows the user to efficiently and effectively manipulate a database. In this
model a database table is a representation of a mathematical relation, that is, a set of
items that share certain characteristics or attributes. Here, each table column represents an
