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Multi-relational data mining in

Microsoft SQL Server 2005

C. L. Curotto

1

& N. F. F. Ebecken

2

& H. Blockeel

3

1

PPGCC/UFPR, Universidade Federal do Paraná, Brazil

2

COPPE/UFRJ, Universidade Federal do Rio de Janeiro, Brazil

3

DTAI/DCS/KUL, Katholieke Universiteit Leuven, Belgium

Abstract

Most real life data are relational by nature. Database mining integration is an

essential goal to be achieved. Microsoft SQL Server (MSSQL) seems to provide

an interesting and promising environment to develop aggregated multi-relational

data mining algorithms by using nested tables and the plug-in algorithm

approach. However, it is currently unclear how these nested tables can best be

used by data mining algorithms. In this paper we look at how the Microsoft

Decision Trees (MSDT) handles multi-relational data, and we compare it with

the multi-relational decision tree learner TILDE. In the experiments we perform,

MSDT has equally good predictive accuracy as TILDE, but the trees it gives

either ignore the relational information, or use it in a way that yields

non-interpretable trees. As such, one could say that its explanatory power is

reduced, when compared to a multi-relational decision tree learner. We conclude

that it may be worthwhile to integrate a multi-relational decision tree learner in

MSSQL.

Keywords: multi-relational, data mining, algorithm, decision trees, databases,

sql server, nested tables.

1 Introduction

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

Data Mining VII: Data, Text and Web Mining and their Business Applications 151

© 2006 WIT Press

Vol 37, WIT Transactions on Information and Communication Technologies,

www.witpress.com, ISSN 1743-3517 (on-line)

doi:10.2495/DATA060151

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