Identifying Emerging Technologies and Influential Companies Using Network Dynamics of Patent Clusters

Michael Tsesmelis (), Ljiljana Dolamic (), Marcus M. Keupp (), Dimitri Percia David () and Alain Mermoud ()
Additional contact information
Michael Tsesmelis: armasuisse Science and Technology
Ljiljana Dolamic: armasuisse Science and Technology
Marcus M. Keupp: Military Academy at the Swiss Federal Institute of Technology Zurich
Dimitri Percia David: University of Applied Sciences Valais
Alain Mermoud: armasuisse Science and Technology

Chapter Chapter 7 in Cyberdefense, 2023, pp 103-122 from Springer

Abstract: Abstract 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.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-30191-9_7

Ordering information: This item can be ordered from
http://www.springer.com/9783031301919

DOI: 10.1007/978-3-031-30191-9_7

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().