An Information Integration Model Study Of Business Intelligence Based On Bayesian Network

The development of Internet stimulates the booming of e-commerce (EC), the relatedenterprises spring up in large amount, at the same time, it also brings pressure and challengesto these enterprises for survival and development. So business intelligence (BI) is introducedinto EC in order to stay competitive. This paper proposes Bayesian network into EC, andresearches the business intelligence information integration model of EC based on Bayesiannetwork.This paper firstly studies the algorithm of Bayesian network structure learning. Learninga General Bayesian Network (GBN) depends on node ordering and correlation of nodes. Thecurrent methods are not efficient enough because of the random node ordering and notconsidering about the degree of independence between nodes. In this paper, we propose a newmethod for structure learning. It introduces the degree of independence between nodes intothe scoring function, and uses genetic algorithm to search for the best ordering. With theimproved scoring function and optimal ordering, the Bayesian Network for the classicaldatasets are structurally learned, and errors are compared. The experiment results show thatour method is more efficient.In view of actual trade situation of EC, we select the main influencing factors of uservalue evaluation, and use the improved algorithm to establish the Bayesian network. Then weextract the main inference rules, and do knowledge inference in the aspects of user evaluation,user source, lost users and potential users. The experiment results show that the inferencecorrect rate is92.4%.Finally, this paper studies the business intelligence information integration model, anddiscusses the application of the model in real-time data monitoring, user behavior analysis,conversion rate of user and user value analysis.