Measuring Technology Maturity / Forschungs-/Entwicklungs-/Innovations-Management (PDF)

Till Albert presents a machine learning based approach to
harnessing information contained in big data from different media sources such
as patents, scientific publications, or the internet. He shows how this information
can be used for automated maturity evaluation of yet unknown technologies. Elaborate
patent based indicators contain very useful information on technological
aspects of maturity but lack for others such as social, economic, ecological,
or political factors. The approach presented in this book is able to
incorporate these other factors and provide a firm basis for robust technology
maturity and speed of maturity evaluation.

Contents

• Information Scattering in Different Text Media
  
• Identifying Text Media Suitable for Informetric Analyses and
  Deriving Relevant Indicator Values
  
• Using Machine Learning to Gauge the Maturity Classification
  Performance of a Set of Indicators
  
• Representation, Interpretation, and Utilization of Maturity
  Analysis Results
  

Target Groups

• Researcher and students of Business Engineering, Informatics,
  and Mathematics
  
• Innovation Managers, Technology Managers, Business
  Intelligence Professionals, Future Researchers
  

Till Albert wrote this dissertation with Professor Martin G.
Moehrle at the Institute of Project Management and Innovation (IPMI) of the
University of Bremen. He now works in the area of data driven approaches to
support innovation and technology management, such as patent analysis,
scientometrics, webometrics, social network analysis, and combinations thereof.