We propose an extension of the Viterbi algorithm that makes second-order hidden Markov models computationally efficient. A comparative study between first-order (HMM1's) and second-order Markov models (HMM2's) is carried out. Experimental results show that HMM2's provide a better state occupancy modeling and, alone, have performances comparable with HMM1's plus postprocessing.

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Model composition is a crucial activity in Model Driven Engineering (MDE). It is particularly useful when adopting a multi-modeling approach to analyze and design software systems. In previous works, we defined a view-based UML profile called VUML. In this paper, we describe a composition process and a MDE-based framework, which contains a generic composition part, and a specific part dedicated to a given modeling domain. To illustrate our approach, we apply it to the composition (merging) of two UML class diagrams into one VUML class diagram. The composition operator is implemented as a ruled-based transformation in ATL.