Authors: Christine Largeron, Sylvie Chalaye
Addresses: Hubert Curien Laboratory (UMR CNRS 5516), Jean Monnet University, 18 rue Prof. Lauras, 42000 Saint-Etienne Cedex 2, France. ' CREUSET, Jean Monnet University, 6, Rue Basse des Rives, 42023 Saint-Etienne Cedex 02, France; EPURES, 46 rue de la telematique, BP 801, 42952 Saint-Etienne Cedex 9, France
Abstract: Today, economic growth depends more and more on innovation and network alliance. In the context of innovation clusters (poles of competency), decision makers need information about the innovation capacity of their territory and the organisation of research activities for a local geographical area (region, department, etc.). The paper presents a local technology intelligence system for measuring the level of knowledge creation and analysing scientific cooperations. This decision support system is based on knowledge discovery in bibliographic databases providing geographical locations. Through the use of data mining algorithms, it helps in analysing the complexity of territorial networks. We illustrate this approach through a case study of an urban area (Sud Loire, City of Saint-Etienne, France).
Keywords: decision making; data mining; text mining; knowledge discovery; bibliographic databases; scientific databases; technology intelligence; technology watch; territorial intelligence; business intelligence; innovation clusters; knowledge creation; scientific cooperation; France.
International Journal of Reasoning-based Intelligent Systems, 2009 Vol.1 No.3/4, pp.164 - 172
Published online: 27 Aug 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article