International audienceEconomic development based on industrialization, intensive agriculture expansion and population growth places greater pressure on water resources through increased water abstraction and water quality degradation [40]. River pollution is now a visible issue, with emblematic ecological disasters following industrial accidents such as the pollution of the Rhine river in 1986 [31]. River water quality is a pivotal public health and environmental issue that has prompted governments to plan initiatives for preserving or restoring aquatic ecosystems and water resources [56]. Water managers require operational tools to help interpret the complex range of information available on river water quality functioning. Tools based on statistical approaches often fail to resolve some tasks due to the sparse nature of the data. Here we describe HydroQual, a tool to facilitate visual analysis of river water quality. This tool combines spatiotem-poral data mining and visualization techniques to perform tasks defined by water experts. We illustrate the approach with a case study that illustrates how the tool helps experts analyze water quality. We also perform a qualitative evaluation with these experts

This paper presents two anonymisation methods to process an SMS corpus. The first one is based on an unsupervised approach called Seek&Hide. The implemented system uses several dictionaries and rules in order to predict if a SMS needs anonymisation process. The second method is based on a supervised approach using machine learning techniques. We evaluate the two approaches and we propose a way to use them together. Only when the two methods do not agree on their prediction, will the SMS be checked by a human expert. This greatly reduces the cost of anonymising the corpus.

This article presents the system Seek&Hide, a text message processing tool developed for the sud4science LR (http://www.sud4science.org/) project. It performs the anonymisation/de-identification of a corpus. At present, it has been used to anonymise the sud4science LR corpus of French text messages collected during the project. This is done in two phases. In the first phase, it automatically processes over 70% of the corpus. The rest of the corpus is processed in the second phase, aided by an expert annotator via a web interface specifically designed to simplify the task.

This article presents the system Seek&Hide, a text message processing tool developed for the sud4science LR (http://www.sud4science.org/) project. It performs the anonymisation/de-identification of a corpus. At present, it has been used to anonymise the sud4science LR corpus of French text messages collected during the project. This is done in two phases. In the first phase, it automatically processes over 70% of the corpus. The rest of the corpus is processed in the second phase, aided by an expert annotator via a web interface specifically designed to simplify the task.