Due to the scale and computational complexity of current simulation codes, metamodels (or surrogate models) have become indispensable tools for exploring and understanding the desi...
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
Several considerable impediments stand in the way of sensor network prototype applications that wish to realize sustained deployments. These are: scale, longevity, data of interest...
Knowledge is power but for interrelated data, knowledge is often hidden in massive links in heterogeneous information networks. We explore the power of links at mining heterogeneou...
— Energy planning and optimization constitutes one of the most significant challenges for high-mobility networks. This paper proposes a novel framework to share, retain and re...