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European Knowledge Discovery Network of Excellence

Objective

Knowledge Discovery or Data Mining is the partially automated process of extracting patterns from databases. It is a scientifically and commercially highly relevant approach for enhancing the intelligence of services. It is important from a European perspective since the EC's eEurope initiative presupposes the successful application of Knowledge Discovery techniques. The EC-JRC's IPTS predicts in its ""Emerging Thematic Priorities for Research in Europe"" that new scientific disciplines will be established in data mining and related areas by 2004. In this sensitive stage, an adequate representation of the growing European KDD community is missing. The objective of KDNet is to fill this gap. It will be an effective means to avoid fragmentation of European research activities, to catch up and to gain leadership with respect to research done in the US in the area of knowledge discovery and to focus early on transfer of knowledge to industry.
Research, industry and public sector organisations participate in KDNet to jointly shape the European knowledge discovery community, giving it a coherent and highly visible profile. International workshops supported by an online information service, training material and a roadmap will implement the objectives. As a result, fragmented research can be unified and industry will have a much more immediate access to state-of-the-art research results, accelerating industrial innovation, increasing competitiveness, and helping to establish the eEurope vision.
Knowledge Discovery or Data Mining is the partially automated process of extracting patterns from databases. It is a scientifically and commercially highly relevant approach for enhancing the intelligence of services. It is important from a European perspective since the EC's eEurope initiative presupposes the successful application of Knowledge Discovery techniques. The EC-JRC's IPTS predicts in its ""Emerging Thematic Priorities for Research in Europe"" that new scientific disciplines will be established in data mining and related areas by 2004. In this sensitive stage, an adequate representation of the growing European KDD community is missing. The objective of KDNet is to fill this gap. It will be an effective means to avoid fragmentation of European research activities, to catch up and to gain leadership with respect to research done in the US in the area of knowledge discovery and to focus early on transfer of knowledge to industry.
Research, industry and public sector organisations participate in KDNet to jointly shape the European knowledge discovery community, giving it a coherent and highly visible profile. International workshops supported by an online information service, training material and a roadmap will implement the objectives. As a result, fragmented research can be unified and industry will have a much more immediate access to state-of-the-art research results, accelerating industrial innovation, increasing competitiveness, and helping to establish the eEurope vision.

DESCRIPTION OF WORK
A consortium of founding members for the network has been set up including some of the most renowned European research institutions in the area of knowledge discovery; small and medium sized companies offering innovative data mining solutions; global players from industry that apply data mining in their core business, e.g. marketing, finance, telecommunication, automotive and transport; public sector organisations in need of enhancing the intelligence of their services. This unique consortium structure allows combining authoritative accounts on the state-of-the-art and future of European Knowledge Discovery research with highly innovative commercial solutions and first-hand accounts on the actual demand of data mining solutions by industry.
KDNt will:
(1) identify and anticipate new emergent technologies and trends in Knowledge Discovery and transfer that knowledge to industry and the public sector;
(2) provide a forum for academic, industry and public sector to establish collaboration;
(3) maintain an information service for industry, and scientists and provide training materials;
(4) represent the European Knowledge Discovery community in Europe and world-wide;
(5) integrate sub- communities from database theory, statistics, machine learning, case-based reasoning and neighbouring disciplines such as bio-informatics, economics, and geographic information systems.
KDNet is an open network. Any interested European research or commercial organisation can apply to become a member node. The basic requirement is to actively participate in the network's activities. All network activities mutually support each other, culminating in a series of major international workshop events that will bring together researchers, industry ad public sector representatives.

Fields of science

Programme(s)

Topic(s)

Call for proposal

Data not available

Funding Scheme

THN - Thematic network contracts

Coordinator

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
EU contribution
No data
Address
HANSASTRASSE 27C
80686 MUENCHEN
Germany

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Total cost
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Participants (32)

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Last update: 13 June 2005

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Permalink: https://cordis.europa.eu/project/id/IST-2001-33086

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