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Abstract: Recent advances in the field of kernel-based machine learning methods allow fast processing of text using string kernels utilizing suffix arrays. kernlab provides both kernel methods’ infrastructure and a large collection of already implemented algorithms and includes an implementation of suffix-array-based string kernels. Along with the use of the text mining infrastructure provided by tm these packages provide R with functionality in processing, visualizing and grouping large collections of text data using kernel methods. The emphasis is on the performance of various types of string kernels at these tasks. [Copyright &y& Elsevier]

The Unified Modelling Language (UML) can be used to specify complex systems: component types are modelled as classes, interdependencies as associations with multiplicities and labels. This paper describes how to handle constraints on associations and multiplicities declaratively by translating them to inequalities over integers without adding complexity. This method provides well-defined semantics and allows for efficient algorithms for reasoning tasks like detecting inconsistencies. We identify some challenges arising from the use of class diagrams for product configuration, and propose solutions for some of them. The paper concludes with the discussion of an example derived from a real-world configuration problem in the railway domain.