Observing the workload on a computer system during a short (but not too short) time interval may lead to distributions that are significantly different from those that would be o...
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
Asma Rabaoui, Nicolas Viandier, Juliette Marais, E...
The steel production of the German company Saarstahl AG, a global respected steel manufacturer, represents a Supply Chain which comprehends several time-critical and highly interf...
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Data Grids have been adopted as the next-generation platform by many scientific communities that need to share, access, transport, process and manage large data collections distri...