International audienceSeven methods designed to delineate homogeneous river segments, belonging to four families, namely -- tests of homogeneity, contrast enhancing, spatially constrained classification, and hidden Markov models -- are compared, firstly on their principles, then on a case study, and on theoretical templates. These templates contain patterns found in the case study but not considered in the standard assumptions of statistical methods, such as gradients and curvilinear structures. The influence of data resolution, noise and weak satisfaction of the assumptions underlying the methods is investigated. The control of the number of reaches obtained in order to achieve meaningful comparisons is discussed. No method is found that outperforms all the others on all trials. However, the methods with sequential algorithms (keeping at order n + 1 all breakpoints found at order n) fail more often than those running complete optimisation at any order. The Hubert-Kehagias method and Hidden Markov Models are the most successful at identifying subpatterns encapsulated within the templates. Ergodic Hidden Markov Models are, moreover, liable to exhibit transition areas

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This paper introduces a method for the automatic acquisition of a rich case representation from free text for process-oriented case-based reasoning. Case engineering is among the most complicated and costly tasks in implementing a case-based reasoning system. This is especially so for process-oriented case-based reasoning, where more expressive case representations are generally used and, in our opinion, actually required for satisfactory case adaptation. In this context, the ability to acquire cases automatically from procedural texts is a major step forward in order to reason on processes. We therefore detail a methodology that makes case acquisition from processes described as free text possible, with special attention given to assembly instruction texts. This methodology extends the techniques we used to extract actions from cooking recipes. We argue that techniques taken from natural language processing are required for this task, and that they give satisfactory results. An evaluation based on our implemented prototype extracting workflows from recipe texts is provided.

Summary 1.It is well established that pollen-mediated gene flow among natural plant populations depends on a complex interaction between the spatial distribution of pollen sources and the short-and long-distance components of pollen dispersal. Despite this knowledge, spatial isolation strategies proposed in Europe to ensure the harvest purity of conventional crops are based on distance from the nearest genetically modified (GM) crop and on empirical data from two-plot experiments. Here, we investigate the circumstances under which the multiplicity of pollen sources over the landscape should be considered in strategies to contain GM crops. 2. We simulated pollen dispersal over eighty 6 × 6 km simulated landscapes differing in field characteristics and in amount of GM and conventional maize. Pollen dispersal was modelled either via a Normal Inverse Gaussian (NIG, currently used for European coexistence studies) or a bivariate Student (2Dt) kernel. These kernels differ in their amount of short-and long-distance dispersal. We used linear models to analyse the impact of local and landscape variables on impurity rates (i.e. proportion of seeds sired by pollen from a transgenic crop) in conventional fields and quantified their increase due to dispersal from other than the closest GM crops. 3. The average impurity rate over a landscape increased linearly with the proportion of GM maize over that landscape. The increase was twice as fast using the NIG kernel and was governed by the short-distance dispersal component. 4. Variation in impurity rates largely depended on the distance to the closest GM crop and the size of the receptor field. However, impurity rates were generally underestimated when only dispersal from the closest GM field was considered. 5. Synthesis and applications. Distance to the closest GM crop had most impact on impurity rates in conventional fields. However, impurity rates also depended on intermediate-to long-distance dispersal from distant GM crops. Therefore, isolation distances as currently defined will probably not allow long-term coexistence of GM and conventional crops, especially as the proportion of GM crops grown increases. We suggest strategies to account for this impact of long-distance dispersal.

Abstract. This paper presents a theoretical framework for exploring temporal data, using Relational Concept Analysis (RCA), in order to extract frequent sequential patterns that can be interpreted by domain experts. Our proposal is to transpose sequences within relational contexts, on which RCA can be applied. To help result analysis, we build closed partially-ordered patterns (cpo-patterns), that are synthetic and easy to read for experts. Each cpo-pattern is associated to a concept extent which is a set of temporal objects. Moreover, RCA allows to build hierarchies of cpo-patterns with two generalisation levels, regarding the structure of cpo-patterns and the items. The benefits of our approach are discussed with respect to pattern structures.

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

In the frame of designing a knowledge discovery system, we have developed stochastic models based on highorder hidden Markov models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the n previous states according to the order of the model. We study the process of achieving information extraction from spatial and temporal data by means of an unsupervised classification. We use therefore a French national database related to the land use of a region, named Ter Uti, which describes the land use both in the spatial and temporal domain. Land-use categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies.The temporal segmentation of the data is done by means of a second-order Hidden Markov Model (HMM2) that appears to have very good capabilities to locate stationary segments, as shown in our previous work in speech recognition. The spatial classification is performed by defining a fractal scanning of the images with the help of a Hilbert-Peano curve that introduces a total order on the sites, preserving the relation of neighborhood between the sites. We show that the HMM2 performs a classification that is meaningful for the agronomists.Spatial and temporal classification may be achieved simultaneously by means of a two levels HMM2 that measures the a posteriori probability to map a temporal sequence of images onto a set of hidden classes. ContextMining sequential and spatial patterns is an active area of research in artificial intelligence. One basic problem in analyzing a sequence of items is to find frequent episodes, i.e. collections of events occurring frequently together. Early in 1995, Agrawal [1,2] proposes non-numeric algorithms for extracting regular patterns from temporal data. Conversely, we present in this paper new numerical algorithms, based on high-order stochastic models -the second-order hidden Markov models (HMM2) -capable to discover frequent sequences of events in temporal and spatial data. These algorithms were initially specified for speech recognition purposes [20] in our laboratory. We show that, with minor changes, they can extract spatial and temporal regularities that can be explained by human experts and may constitute some atoms of knowledge [11].The HMM2's are based on the probabilities and statistics theories. Their main advantage is the existence of unsupervised training algorithms that allow to estimate a model parameters from a corpus of observations and an initial model. Several criteria are used : the maximum likelihood criterium implemented in the well known EM algorithm [14], the maximum a posteriori criterium [17] and the maximum mutual information criterium [5]. Rabiner [29] gives an extensive description of analytical learning methods for HMM. Other algorithms [28,30], based on stochastic likelihood maximization and Bayesian estimation have shown interesting ...

International audienceQualitative algebras form a family of languages mainly used to represent knowledge depending on space or time. This paper proposes an approach to adapt cases represented in such an algebra. A spatial example in agronomy and a temporal example in cooking are given. The idea behind this adaptation approach is to apply a substitution and then repair potential inconsistencies, thanks to belief revision on qualitative algebras.Les algèbres qualitatives forment une famille de langages utilisés principalement pour représenter des connaissances de nature temporelle ou spatiale. Cet article propose une approche pour adapter des cas représentés dans une telle algèbre. Un exemple spatial portant sur l'agronomie ainsi qu'un exemple temporel portant sur la cuisine sont donnés. L'idée sous-jacente à cette approche de l'adaptation est d'appliquer une substitution puis de réparer les incohérences qui pourraient être apparues, grâce à la révision des croyances appliquée aux algèbres qualitatives