The information society with its all-digital information content, and the advent of HPCN technology to support huge databases, presents users with the problem of interpreting vast amounts of data. Although theoretical work and methodological approaches have been published, data mining (or knowledge discovery in databases) at present is more of an art than a well understood reliable process. There exists no well understood, practical data mining process. This fact particularly hinders data mining projects involving huge databases and is seen as barrier to the profitable widespread deployment of HPCN. The project aims to cater for data mining needs of industrial users of huge data warehouses, by providing an industry-neutral and tool-neutral process model. This project will develop a data mining process which is fast, well understood, reliable, and valid across a wide range of applications.
Starting from the embryonic knowledge discovery processes used in industry today and responding directly to user requirements, this project will define and validate a data mining process that is generally applicable in diverse industry sectors. This will make large data mining projects faster, more efficient, more reliable, more manageable, and less costly. A widely adopted process should foster the development of a multitude of data mining tools which support it, thereby significantly contributing to promote a profitable use of HPCN technology.
A "special interest group" (SIG) of users and suppliers will be formed to broaden the basis for development and testing without sacrificing the efficiency and effectiveness of a small, tightly-focused consortium. The SIG is a key feature of this project, helping to ensure relevance and applicability of the results, and facilitating dissemination and exploitation.
The process model developed by the project will be exploited by the data warehouse vendor and the data mining tool supplier to enhance their product and service offerings. The user partners will exploit the results of the project internally to improve their business intelligence and decision making.
Aufforderung zur VorschlagseinreichungData not available
FinanzierungsplanData not available
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