Introduction Administrative coding of medical diagnoses in intensive care unit (ICU) patients is mandatory in order to create databases for use in epidemiological and economic studies. We assessed the reliability of coding between different ICU physicians.

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Multi-sources information systems, such as data warehouse systems, involve heterogeneous sources. In this paper, we deal with the semantic heterogeneity of the data instances. Problems may occur when confronting sources, each time different level of denominations have been used for the same value, e.g. "vermilion" in one source, and "red" in an other. We propose to manage this semantic heterogeneity by using a linguistic dictionary. "Semantic operators" allow a linguistic flexibility in the queries, e.g. two tuples with the values "red" and "vermilion" could match in a semantic join on the "color" attribute. A particularity of our approach is it states the scope of the flexibility by defining classes of equivalent values by the mean of "priority nodes". They are used as parameters for allowing the user to define the scope of the flexibility in a very natural manner, without specifying any distance.

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues.