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

Due to the increasingly difficulty of discovering patterns in real-world databases using only conventional OLAP tools, an automated process such as data mining is currently essential. As data mining over large data sets can take a prohibitive amount of time related to the computational complexity of the algorithms, parallel processing has often been used as a solution. However, when data does not fit in memory, some solutions do not apply and a database system may be required rather than flat files. Most implementations use a database system loosely-coupled with the data mining algorithms. We address the data consuming activities through parallel processing and data fragmentation on the database server, providing a tight integration with data mining techniques. Experimental results show that the potential benefits of this integration were obtained, despite the difficulties of processing a complex application