Statistical analysis of large sets of models

Many applications in Model-Driven Engineering involve processing multiple models, e.g. for comparing and merging of model variants into a common domain model. Despite many sophisticated techniques for model comparison, little attention has been given to the initial data analysis and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. We would like to develop a generic approach for model comparison and analysis for large datasets; using techniques from information retrieval, natural language processing and machine learning. We are implementing our approach as an open framework and have so far evaluated it on public datasets involving domain analysis, repository management and model searching scenarios.