Accepted 31 December 2011
Available online 2 February 2012
Keywords:
Data acquisition
Databases
Data warehouse
Data mining
Data miner
Intelligent miner

a b s t r a c t
In today’s information society, we witness an explosive growth of the amount of information becoming
available in electronic form and stored in large databases. Data mining can help in discovering knowledge.
Data mining can dig out valuable information from databases in approaching knowledge discovery
and improving business intelligence. In this paper, we have discussed the involvement and effect of data
mining techniques on relational database systems, and how its services are accessible in databases, which
tool we require to use it, with its major pros and cons in various databases. Through all this discussion we
have presented how database technology can be integrated to data mining techniques.
 2012 Elsevier Ltd. All rights reserved.
1. Introduction
In recent years data mining has become a very popular technique

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for extracting information from the database in different
areas due to its flexibility of working on any kind of databases
and also due to the surprising results [1].
Data mining is the search for valuable information in large
volumes of data [1]. With the increase of availability of databases
containing structures, mining techniques especially designed for
this type of data are becoming more and more important [2]. To
improve both software productivity and quality, software engineers
are increasingly applying data mining algorithms to various
Software Engineering tasks [3].
The progress in data acquisition and successful development of
storage technology at cheaper rates, along with limited human
capabilities in analyzing and understanding big databases have
tempted scientists and researchers to move forward towards the