U-BASE: General Bayesian Network-Driven Context Prediction for Decision Support

We propose a new type of ubiquitous decision support system that is powered by a General Bayesian Network (GBN). Because complicated decision support problems are plagued by complexities when interpreting causal relationships among decision variables, GBNs have shown excellent decision support competence because of their flexible structure, which allows them to extract appropriate and robust causal relationships among target variables and related explanatory variables. The potential of GBNs, however, has not been sufficiently investigated in the field of ubiquitous decision support. Hence, we propose a new type of ubiquitous decision support mechanism called U-BASE, which uses a GBN for context prediction in order to improve decision support. To illustrate the validity of the proposed decision support mechanism, we collected a set of contextual data from college students and applied U-BASE to induce useful and robust results. The practical implications are fully discussed, and issues for future studies are suggested.