Title: A decision support system for identification of technology innovation risk based on sequential CBR
Authors: Quan Xiao
Addresses: School of Information Technology, Jiangxi University of Finance and Economics, Mailuyuan District, Nanchang, Jiangxi, 330032, China
Abstract: 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.
Keywords: risk identification; case-based reasoning; technology innovation; sequential data; decision support system.
International Journal of Information Technology and Management, 2019 Vol.18 No.1, pp.47 - 62
Received: 25 Nov 2016
Accepted: 22 May 2017
Published online: 13 Feb 2019 *