Technology innovation risk identification based on sequential CBR

To identify risks in the increasingly complex market is an important issue related to the survival and development of technology innovation enterprises. In front of diversified and massive risk sources, it is an inevitable choice for technology innovation enterprises to utilize IT to support the risk identification task. Case-Based Reasoning(CBR) is an analogical reasoning based AI method, and can help enterprises to identify new risks from past technology innovation risk cases, but have limitations for the problems with dynamic characteristics. Under this background, we study the sequential CBR method for technology innovation risk identification in the work, where cases are represented as risk event sequences and the case similarity measuring method based on weighted event sequence pattern mining is proposed. Finally the effectiveness of the method is demonstrated by a risk identification case.