Author(s): C. L. Curotto & N. F. F. Ebecken
One of the most useful features of the Object Linking and Embedding Database
for Data Mining technology, a standard for implementation of data mining
algorithms aggregated with Microsoft® SQL Server™, is the ability of using
nested data mining columns, providing an affordable and efficient way to
support relational data models.
A brief overview of the common approaches used
to deal with multi-relational data mining is presented.
Experiments are carried
out, using the SQL Server™ 2000 release as well as its new 2005 Beta 2 version,
to evaluate the capability of these tools while dealing with multi-relational data
In these experiments the Microsoft® Decision Trees data mining
algorithm is considered.
multi-relational, data mining, algorithm, decision trees, database,
sql, server, nested tables.
To achieve the tight coupling of Data Mining (DM) techniques in Database
Management Systems (DBMS) technology, a number of approaches have been
developed in the last years.
These approaches include solutions provided by both
company and academic research groups.
Toward this objective, the Microsoft® (MS) Object Linking and Embedding
Database for DM (OLE DB DM) technology  provides an industry standard
for developing DM algorithms.
This technology was included in the MS SQL
Server™ (MSSQL) 2000 release .
The Analysis Services (AS) component of
this software includes a DM provider supporting two algorithms: one for
classification by decision trees  and another for clustering .
Size: 1,017 kb
Paper DOI: 10.2495/DATA050221
the Full Article
This article is part of the WIT OpenView scheme and you can download the full text Adobe PDF article for FREE by clicking the 'Openview' icon below.
this page to a colleague.
This paper can be found in the following bookData Mining VI: Data Mining, Text Mining and their Business Applications Buy