scholarly journals Data mining: a tightly-coupled implementation on a parallel database server

2002 ◽  
Author(s):  
M. Sousa ◽  
M. Mattoso ◽  
N.F.F. Ebrecken
Keyword(s):  
Data Mining ◽  
Parallel Database ◽  
Database Server ◽  
Tightly Coupled

Mining Associations Rules on a NCR Teradata System

2011 ◽  
pp. 746-751
Author(s):  
Soon M. Chung ◽  
Murali Mangamuri
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Database Systems ◽  
Database System ◽  
Parallel Database ◽  
Parallel Database Systems ◽  
Mining Association Rules

Data mining from relations is becoming increasingly important with the advent of parallel database systems. In this paper, we propose a new algorithm for mining association rules from relations. The new algorithm is an enhanced version of the SETM algorithm (Houtsma & Swami 1995), and it reduces the number of candidate itemsets considerably. We implemented and evaluated the new algorithm on a parallel NCR Teradata database system. The new algorithm is much faster than the SETM algorithm, and its performance is quite scalable.

Mining a large database with a parallel database server

1999 ◽  
Vol 3 (6) ◽  
pp. 437-451 ◽  
Author(s):  
M DESOUSA ◽  
M MATTOSO ◽  
N EBECKEN
Keyword(s):  
Large Database ◽  
Parallel Database ◽  
Database Server

Building Data Mining Models in the Oracle 9i Environment

10.28945/2697 ◽  
2003 ◽  
Author(s):  
Krzysztof Hauke ◽  
Mievzyslaw L. Owoc ◽  
Maciej Pondel
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
Knowledge Bases ◽  
Crucial Issue ◽  
Knowledge Based ◽  
Whole Process ◽  
Database Server ◽  
Testing Data ◽  
Verification Techniques

Data Mining (DM) is a very crucial issue in knowledge discovery processes. The basic facilities to create data mining models were implemented successfully on Oracle 9i as the extension of the database server. DM tools enable developers to create Business Intelligence (BI) applications. As a result Data Mining models can be used as support of knowledge-based management. The main goal of the paper is to present new features of the Oracle platform in building and testing DM models. Authors characterize methods of building and testing Data Mining models available on the Oracle 9i platform, stressing the critical steps of the whole process and presenting examples of practical usage of DM models. Verification techniques of the generated knowledge bases are discussed in the mentioned environment.

F011006 Data mining with massive parallel database "Teradata"

2012 ◽  
Vol 2012 (0) ◽  
pp. _F011006-1-_F011006-4
Author(s):  
Shinya MATSUMOTO ◽  
Bunichi TAJIMA
Keyword(s):  
Data Mining ◽  
Parallel Database

Mining a large database with a parallel database server

1999 ◽  
Vol 3 (6) ◽  
pp. 437-451 ◽  
Author(s):  
Mauro Sérgio R. de Sousa ◽  
Marta Mattoso ◽  
Nelson F.F. Ebecken
Keyword(s):  
Large Database ◽  
Parallel Database ◽  
Database Server
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