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European Knowledge Discovery Network of Excellence

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.

Leaflet | Map data © OpenStreetMap contributors, Credit: EC-GISCO, © EuroGeographics for the administrative boundaries

Coordinator

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.

Address

Hansastrasse 27c
80686 Muenchen

Germany

Administrative Contact

Michael MAY (Dr)

Participants (32)

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CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS

Spain

CZECH TECHNICAL UNIVERSITY IN PRAGUE

Czechia

DAIMLERCHRYSLER AG

Germany

ERASMUS UNIVERSITEIT ROTTERDAM

Netherlands

EUROPEAN COMMISSION - JOINT RESEARCH CENTRE

Italy

HELSINKI UNIVERSITY OF TECHNOLOGY

Finland

INFRATEST BURKE GMBH & CO

Germany

INSTITUT JOZEF STEFAN

Slovenia

INTERNATIONAL KNOWLEDGE DISCOVERY INSTITUTE PTY.LTD.

Australia

KATHOLIEKE UNIVERSITEIT LEUVEN

Belgium

KUNGLIGA TEKNISKA HOEGSKOLAN

Sweden

LATVIJAS CENTRALA STATISTIKAS KOMITEJA / CENTRAL STATISTICAL BUREAU OF LATVIA

Latvia

OTTO-VON-GUERICKE-UNIVERSITAET MAGDEBURG

Germany

PEROT SYSTEMS NEDERLAND B.V.

Netherlands

PHARMADM NAAMLOZE VENNOOTSCHAP

Belgium

PIXELPARK AG

Germany

PRUDENTIAL SYSTEMS SOFTWARE GMBH

Germany

RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITAET BONN

Germany

ROYAL HOLLOWAY AND BEDFORD NEW COLLEGE

United Kingdom

SCHWEIZERISCHE LEBENSVERSICHERUNGS- UND RENTENANSTALT

Switzerland

TECHNISCHE UNIVERSITAET CHEMNITZ

Germany

THE UNIVERSITY COURT OF THE UNIVERSITY OF ABERDEEN

United Kingdom

UNIVERSIDADE DO PORTO

Portugal

UNIVERSITA DEGLI STUDI DEL PIEMONTE ORIENTALE AMEDEO AVOGADRO

Italy

UNIVERSITA DEGLI STUDI DI BARI

Italy

UNIVERSITAET DORTMUND

Germany

UNIVERSITE DE PARIS XI PARIS-SUD

France

UNIVERSITEIT UTRECHT

Netherlands

UNIVERSITEIT VAN AMSTERDAM

Netherlands

UNIVERSITY OF BRISTOL

United Kingdom

UNIVERSITY OF ULSTER

United Kingdom

VYSOKA SKOLA EKONOMICKA V PRAZE

Czechia

Project information

Grant agreement ID: IST-2001-33086

  • Start date

    1 January 2002

  • End date

    31 December 2004

Funded under:

FP5-IST

  • Overall budget:

    € 903 000

  • EU contribution

    € 888 000

Coordinated by:

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.

Germany