A decision support system for identification of technology innovation risk based on sequential CBR. (2019) - Xiao, Quan
To identify risks in the increasingly complex market is an important issue for the development of technology innovation enterprises. But it is contended that there is still a lack of effective methods to support the dynamic characteristics and knowledge reuse of the problem. In front of a variety of risk sources, utilisation of IT is necessary, and we introduce case-based reasoning (CBR) technique to identify new risks from cases in the past. However, extant CBR method has limitations on problems with dynamic characteristics. This paper provides insights into the dynamic nature of technology innovation risk identification, and designs a decision support system for identification of technology innovation risk, which contributes a novel extension of CBR to sequential CBR. In our framework, cases are represented as sequences of risk events, and similarity between cases is measured based on weighted event sequence pattern mining. The effectiveness of this work is finally illustrated with a case.