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.