Designing a Ubiquitous Decision Support Engine for Context Prediction: General Bayesian Network Approach

Ubiquitous decision support systems have remained an imaginary and almost useless system for decades since its first introduction in early 1990’s. However, it came out of lab into real world as ubiquitous computing became tangible in the form of mobile devices, pervasive mechanisms, and various mobile Internet technologies. Typically, context-aware systems had received acclaims from both researchers and practitioners as an alternative to making ubiquitous systems touch-and-feel electronics to the users. Nevertheless, context-aware systems lack predictive power which is essential for any ubiquitous systems to suggest timely and effective information for users. Poorly predicted information is likely to degrade the ubiquitous systems seriously. In this respect, context prediction mechanism emerges as a reliable vehicle for making ubiquitous systems more sustainable decision support tool for users. Despite the potentials of context prediction mechanism, few reliable mechanisms exist in literature which shows robust performance against changes in user’s contexts. For this reason, we propose a new type of ubiquitous decision support system that is powered by General Bayesian Network (GBN) capable of organizing causal relationships among a set of related variables. Drawing on the GBN’s strengths, this study proposes U-BASE (Ubiquitous Bayesian network-Assisted Support Engine) to suggest more reliable solution for the context prediction tasks. Performance of U-BASE was tested against real contextual data set, garnering very robust results. The practical implications are fully discussed with some future research issues.