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The CRITIKAL project has enabled two large corporate users (Lloyds-TSB and Gehe) to gain a detailed knowledge about how data mining technology can be applied to enhance their capability to use large volumes of corporate information to support business decision making and to derive business knowledge from data. This has been achieved through the development of prototypes of client server data mining software working in the business environment against large scale data warehouses. It has also been achieved through a number of requirement capture tasks and the development of a life cycle methodology for the data mining process.
The results of CRITIKAL can be used by any organisation wishing to use data mining to derive patterns from their large corporate data warehouses. The CRITIKAL architecture is flexible enough to be adapted to the IT infrastructure of any large corporate user.
CRITIKAL uses a three tier data mining architecture for the induction of trees and the discovery of association rules in data. This architecture consists of a client based tree / rules generator, a middle tier contingency and frequency server, and the database (warehouse). This architecture allows the data to be mined in situ and it can exploit the large bandwidth available between the middle-tier and the warehouse, and the limited bandwidth available between the client and the middle tier. CRITIKAL can work against any database but is currently optimised to work against Oracle and Teradata.
Project URL : http://www.pac.soton.ac.uk/critikal/
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