Adaptation of language model of Information Retrieval for empty answers Problem in databases

International audienceInformation over the web is increasingly retrieved from relational databases in which the query language is based on exact matching, data fulfil completely the query or not. The results returned to the user contain only tuples that satisfy the conditions of the query. Thereby, the user can be confronted to the problem of empty answers in the case of too selective query. To overcome this problem, several approaches have been proposed in the literature in particularly those based on query conditions relaxation. Others works suggest the use of fuzzy sets theory to introduce a flexible queries. Another line of research proposes the adaptation of information retrieval (IR) approaches to get an approximate matching in databases. We discuss in this paper, an adaptation of language model of IR to deal with empty answers. The main idea behind our approach is that instead of returning an empty response to the user, a ranked list of tuples that have the most similar values to those specified in user's query is returned

The chain-reentrant shop with the no-wait constraint

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Adaptation du modèle de langue pour le tri des réponses dans les BD

International audienceL'information sur le web est de plus en plus extraite depuis des bases de données (BD) où les langages d'interrogation sont basés sur une recherche exacte. L'utilisateur se trouve confronté au problème de réponses nombreuses lorsque sa requête est peu sélective. Pour remédier à ce problème, plusieurs approches ont été proposées, à l'instar de celles utilisant les techniques de relaxation des requêtes. D'autres travaux proposent de classifier les résultats. Une autre classe d'approches, au quelle on s'intéresse, suggère l'adaptation des techniques de la recherche d'information (RI) pour trier les résultats dans les BD. On propose dans cet article, une adaptation du modèle de langue de la RI pour trier les tuples retournés selon leur score de pertinence vis-à-vis la requête. Ce score est évalué par un modèle de langue bi-gramme qui combine, à travers un lissage par interpolation, les probabilités d'occurrence des valeurs des attributs dans l'ensemble des tuples retournés ainsi que dans la BD. Nous avons évalué l'efficacité de notre approche sur une table contenant 16842 tuples. Les résultats préliminaires obtenus montrent l'intérêt d'exploiter les dépendances entre les valeurs d'attributs

Réseaux bayésiens jumelés et noyau de Fisher pondéré pour la classification de documents XML

In this paper, we are presenting a learning model for XML document classification based on Bayesian networks. Then, we are proposing a model which simplifies the arborescent representation of the XML document that we have, named coupled model and we will see that this approach improves the response time and keeps the same performances of the classification. Then, we will study an extension of this generative model to the discriminating model thanks to the formalism of the Fisher’s kernel. At last, we have applied a ponderation of the structure components of the Fisher’s vector. We finish by presenting the obtained results on the XML collection by using the CBS and SVM method

Réseaux bayésiens jumelés et noyau de Fisher pondéré pour la classification de documents XML

International audienceIn this paper, we are presenting a learning model for XML document classification based on Bayesian networks. Then, we are proposing a model which simplifies the arborescent representation of the XML document that we have, named coupled model and we will see that this approach improves the response time and keeps the same performances of the classification. Then, we will study an extension of this generative model to the discriminating model thanks to the formalism of the Fisher’s kernel. At last, we have applied a ponderation of the structure components of the Fisher’s vector. We finish by presenting the obtained results on the XML collection by using the CBS and SVM method

A maximum diversity-based path sparsification for geometric graph matching

International audienceThis paper presents an effective dissimilarity measure for geometric graphs representing shapes. The dissimilarity measure is a distance that combines a sparsification of the geometric graph based on the maximum diversity problem and a new node embedding that captures the topological neighborhood of nodes. The sparsification step aims to correct the misdistribution of nodes on the geometric graph induced by the noise of image handling. Computational experiments on two popular datasets indicate that our approach retains the form of the shapes while decreasing the number of processed nodes which yields interesting results both on accuracy and time processing 1

Dynamic Dense Subgraph Mining: A new approach for temporal graph summarization

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A simple graph embedding for anomaly detection in a stream of heterogeneous labeled graphs

International audienceIn this work, we propose a new approach to detect anomalous graphs in a stream of directed and labeled heterogeneous edges. The stream consists of a sequence of edges derived from different graphs. Each of these dynamic graphs represents the evolution of a specific activity in a monitored system whose events are acquired in real-time. Our approach is based on graph clustering and uses a simple graph embedding based on substructures and graph edit distance. Our graph representation is flexible and updates incrementally the graph vectors as soon as a new edge arrives. This allows the detection of anomalies in real-time which is an important requirement for sensitive applications such as cyber-security. Our implementation results prove the effectiveness of our approach in terms of accuracy of detection and time processing