A Bayesian network based decision support system

In this highly dynamic environment, data and information are rapidly changing in time. For a decision support system, to capture continuum data and then to extract knowledge from data is in high demand. Our research work focus on developing a systematic approach for discovering causal relations among data and dynamic updating upon to the varying goals. Bayesian network is a powerful tool for representing knowledge and tackling the above inquires. In this research, the entropy-based searching mechanism designed on Bayesian network is adopted to learn the structure of a goal-specific mission. Link strength and connection strength are the measures for selecting links to be added and subtracted. However, these two measures are the relative measures instead of absolute thresholds. Initially, we designed a link/connection strength selecting method for possible adjacency matrices in tabu searching. In the second phase, we used a homogeneity test to ensure the quality of the learned structure. The results of this study will be an important stepping stone to attain the learning requirements of designing, building, and operating effective decision support systems under dynamic environment.