Identifying emerging technologies and influential companies using network dynamics of patent clusters
Tsesmelis, Michael; Dolamic, Ljiljana; Keupp, Marcus Matthias; Percia David, Dimitri; Mermoud, Alain
2023
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Résumé

The need for dependable and real-time insights on technological paradigm shifts requires objective information. We develop a lean recommender system which predicts emerging technology by a sequential blend of machine learning and network analytics. We illustrate the capabilities of this system with patent data and discuss how it can help organizations make informed decisions.

Détails

Titre Identifying emerging technologies and influential companies using network dynamics of patent clusters
Auteur(s)/ trice(s) Tsesmelis, Michael (Cyber-Defence Campus, armasuisse Science and Technology, Thun, Switzerland)
Dolamic, Ljiljana (Cyber-Defence Campus, armasuisse Science and Technology, Thun, Switzerland)
Keupp, Marcus Matthias (Military Academy at the Swiss Federal Institute of Technology Zurich, Birmensdorf, Switzerland)
Percia David, Dimitri (School of Management, HES-SO, University of Applied Sciences and Arts Western Switzerland Valais)
Mermoud, Alain (Cyber-Defence Campus, armasuisse Science and Technology, Thun, Switzerland)
Editeur(s) scientifique(s) Keupp, Marcus Matthias ; Department Head, Department of Defense Economics, Military Academy at the Swiss Federal Institute of Technology, Zurich, Switzerland
Date 2023-09
Publié dans Cyberdefense : the next generation
Publié par Cham, Springer
Pagination pp. 103–122
ISBN 978-3-031-30190-2
DOI https://doi.org/10.1007/978-3-031-30191-9_7
ISSN 0884-8289
Collection et n° International series in operations research & management science, vol. 342
Domaine Economie et Services
Ecole HEG-VS
Institut Institut Entrepreneuriat & Management
Le document apparaît dans Chapitres de livres
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