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International audience; Requirements are usually "hand-written" and suffers from several problems like redundancy and inconsistency. The problems of redundancy and inconsistency between requirements or sets of requirements impact negatively the success of final products. Manually processing these issues requires too much time and it is very costly. The main contribution of this paper is the use of k-means algorithm for a redundancy and inconsistency detection in a new context, which is Requirements Engineering context. Also, we introduce a filtering approach to eliminate "noisy" requirements and a preprocessing step based on the Natural Language Processing (NLP) technique to see the impact of this latter on the k-means results. We use Part-Of-Speech (POS) tagging and noun chunking to detect technical business terms associated to the requirements documents that we analyze. We experiment this approach on real industrial datasets. The results show the efficiency of the k-means clustering algorithm, especially with the filtering and preprocessing steps. Our approach is using the software SEMIOS and will be integrated as a new functionality.

International audience; Incorporating user interest evolution over time is a crucial problem in user profiling. We particularly focus on social profiling process that uses information shared on user social network to extract his/her interests. In this work, we apply our existing time-aware social profiling method on Twitter. The aim of this study is to measure the effectiveness of our approach on this kind of social network platform, which have different characteristics from other social networking sites. Although the improvement compared to the time-agnostic baseline method is still low, the experiments using a parametric study enabled us to show the benefit of applying a time-aware social profiling process on Twitter. We also found that our method performs well on sparse networks. We also found that the information dynamic influenced more the quality of our proposed time-aware method than the relationships dynamic while building the social profile on Twitter. This observation will lead us to a more complex study to find out meaningful factors to incorporate user interest evolution on social profiling process in such a network.

International audience; This paper presents our work about assisting video-surveillance agents in the search for particular video scenes of interest in transit network. This work has been developed based on requirements defined within different projects with the French National Police in a forensic goal. The video-surveillance agent inputs a query in the form of a hybrid trajectory (date, time, locations expressed with regards to different reference systems) and potentially some visual descriptions of the scene. The query processing starts with the interpretation of the hybrid trajectory and continues with a selection of a set of cameras likely to have filmed the spatial trajectory. The main contributions of this paper are: (1) a definition of the hybrid trajectory query concept, trajectory that is constituted of geometrical and symbolic segments represented with regards to different reference systems (e.g., Geodesic system, road network), (2) a spatio-temporal filtering framework based on a spatio-temporal modeling of the transit network and associated cameras.

International audience; This paper represents a study along the cutting edge of the current analysis of online social network in relation with the contents communicated among users. Twitter data is carefully selected around a fixed hash-tag in order to study the specified content in relation with other contents that users bring to connection. A separate network of hash-tags related (in tweets) is constructed for different days; the networks are analyzed within advanced Gephi package, providing several measures -degree, betweenness centrality, communities, as well as the longest path, by which the evolution of communication around specified concepts is quantified. Our study is absolutely in the current trend of analysis of online social networks that, going beyond mere topology, reveals relevant linguistic and social categories and their dynamics.

Several studies demonstrate effectiveness and benefits of using user's social network information to enrich user's profile. In this context, one of our contributions [1] proposes an algorithm enabling to compute user's interests using information from egocentric network extracted communities. Therefore, mining information from a small or a sparse network remains challenging because there is not enough information to enrich a relevant user's profile. So, one of the main lock is to cope with the lack of information that is considered as an important issue to extract a relevant community and could lead to misinterpretations in the user's profile modeling process. We aim to improve the performance of [1], regarding the lack of information problem, in the case of a small and/or a sparse network. We propose to add more information (i.e. relations) into user's network before extracting the data and enriching his profile. To achieve this enrichment, we suggest using snowballsampling technique to identify and add user's distance-2 neighbors (friends of a friend) into the user's egocentric network. Our experimentation conducted in DBLP demonstrates the interest of node integration into small and sparse network. This leads to the study of link prediction that enables us to provide better performances and results compared to the existing work.

International audience; In a social context, the user is more and more an active contributor for producing social information. Then, he needs a tailored information reflecting his current needs and interests in every period of time. This aims to provide a better adaptation while accessing the information space by integrating users' interests dynamic. Indeed, users' interests may change and become “outdated” through time. So, an interest judged as relevant in a period of time may fluctuate in the next period of time. Moreover, analysing the classic user behaviour to deduce his current interests is a difficult task. In fact, his behaviour isn't always reflecting his real interests. In this paper, we propose a new approach for enriching the user profile in an evolutionary environment such as a social network. The enrichment takes into account: i) the social behaviour and more precisely the tagging behaviour (that reflects user's interests) and ii) the temporal information (that reflects the dynamic evolution of users' interests). Our approach focus on the concept of temperature that reflects the importance of a resource in each period of time. This concept is used to infer common interests of users tagging the same “important” resource. The originality of our approach relies on combining information tags, users and resources in a way that guarantees a better enrichment for the social user profile. Our approach has been tested and evaluated with the Delicious social database and shows interesting precision values.

National audience; With the great diffusion of multimedia information on the web, plenty of solutions are proposed to solve the web users' cognitive overload. Despite the multitude of solutions, the web users are still looking for more and more simple assistant in web using. In this paper we present an information adaptation system using characteristics of the user. Our work aims improving access to information through recommendations made by system users with access to information. After introducing the concepts and mechanisms used by our system, we compare it with other systems in order to show the originality of our ideas.

Nowadays, social networks are more and more widely used as a solution for enriching users' profiles in systems such as recommender systems or personalized systems. For an unknown user's interest, the user's social network can be a meaningful data source for deriving that interest. However, in the literature very few techniques are designed to meet this solution. Existing techniques usually focus on people individually selected in the user's social network, and strongly depend on each author's objective. To improve these techniques, we propose to use a community based algorithm that is applied to a part of the user's social network (egocentric network) and that can be reused for any purpose (e.g. personalization, recommendation). We compute weighted user's interests from these communities by considering their semantics (interests related to communities) and their structural measures (e.g. centrality measures) in the egocentric network graph. A first experiment conducted in Facebook demonstrates the usefulness of this technique compared to individuals based techniques, and the influence of structural measures (related to communities) on the quality of derived profiles. The results also raise the problem of users' privacy in platforms such as online social networks. To enable users to better protect their privacy, these platforms should provide their users with a way to also make their friendlist private.

This paper presents a framework for the management, the processing and the reuse, of information relative to defects. This framework is based on the fact that each defect triggers a resolution process in which information about the detected incident (i.e. the problem) and about the applied protocol to resolve it (i.e. the solution) is collected. These different types of information are the cornerstone of the optimization of corrective and preventive processes for new defects. Experimentations show that our prototype provides a very satisfactory quality of results with good performances.

In this paper, we are interested in the context of the French government's new efforts to put into practice the concept of the "personal health record (PHR)". We propose an ontology-based solution to improve the access to PHR and to extend it with the annotations that the patient can add and that will constitute the Annotated PHR. We consider that each user can manage the annotations access policies. We also develop an ontology-based solution for granting medical agents with useful, authorized and relevant information, which adopts XACML query rewriting mechanisms. The paper illustrates how the proposed framework could assist patients during their travels, when an unexpected health disorder takes place and the suitable specialist is not available.

The current paper expose a technique for developing a solution of personalized recommendations for e-learning systems adopting an ontology-based modeling of user profiles and document models. Because the solution is situated at the interference of three domains (e-learning, semantic Web and adaptive hypermedia systems), the methodological aspects considered in developing such a solution are discussed with respect to the existing techniques in these domains. As well, some evaluation criteria of such solution are discussed, while considering some existing systems that have similar characteristics to the proposed solution.

The available amount of geographic datasets has considerably grown. This is due to the increasing number of different devices used in collecting such data. Moreover, the amazing progress of the web that allows sharing and accessing to any type of information has further increased this availability. However, query evaluation in spatial data is often expensive, because these data have complex structures and they are available in different formats and scales. Recently, systems have been developed to allow users to sketch what they are looking for instead of translating their requests into verbal query statements. These systems try to close the gap between user and spatial data, so the user can focus on his query rather than spending his time formulating the request. With an intention to make this process easy, the spatial data, in the database, must be reformulated to answer this new type of request. This paper presents a method for remodelling the spatial data in database in order to establish a retrieval information system by sketching.

In this article we are presenting a solution for the problem of combining various indexation algorithms in order to acquire a semantic multimedia indexation and to provide responses to the user complex queries. The challenge of this problem concerns the big heterogeneity of the multimedia indexation algorithms and the weak semantic aspect they address. Our solution considers a generic interface for the indexation algorithms, an implementation as Web services, as well as a semantic description in terms of WSMO (Web Service Modeling Ontology) of their functionality and orchestration. Original contribution of the article concerns the idea of organizing the various multimedia metadata types into a generic structure,used to express the user queries, the algorithms' generic interface, as well as the algorithms' WSMO metadata. This approach facilitates the definition of algorithm combination rules, and enables the reduction of the multimedia retrieval task to a metadata matching process.

The diversity of terminals used by the user to access resources (Personal Digital Assistant, mobile phone, etc.) using several types of networks (wireless, local, etc.) generates a growing need to adapt services dynamically to the user's context. In this article, we present our architecture that aims at adapting content and presentation of services to the user's context. We realize the content adaptation based on a new data modeling methodology. This methodology aims to take into account the different structures associated with the same data allowing a data representation in several viewpoints. The presentation adaptation is based on an automatic generation process of the interfaces service code. The context in our architecture is presented by a generic model for the user and the service. The adaptation process is detailed based on an e-learning scenario.

The advancement of mobile information systems is going in parallel with Geographical Information Systems where they both meet in providing location-aware services. New systems are providing a new pervasive GIS dimension where access to geographical attributes is being possible "anytime, anyplace and anyhow". In this article, we propose a system that provides mobile users with a service that corresponds not only to their requests but also to their preferences, location and querying time. Finally, we project the query results to construct a GIS map layer that is added to original GIS area map in order to provide more indications to the path that the user should follow in order to reach the demanded service.

Nowadays, with the great diffusion of mobile technology, and ubiquitous systems, the context has become the ear and the eye of information systems. These systems are more and more based on the usage of Web services which are designed to support interoperable application-to-application interaction over the Web. In this article, we present our contribution aims at proposing a new architecture of Web information systems, supporting the adaptation process to the user context and returning to the user a list of Web services adapted to his context. This architecture is based on an extension of AHA! architecture adding an adaptation layer containing various components dedicated to the context adaptation. So, our aim is to build an adaptive Web information system based on Web services. Finally, we present an algorithm which we use to build a user contextual profile "profile-context-user (UCP)" containing only the user preference that can be satisfied with the context.

In the age of mobility, many times a certain teacher, a student or a document face difficulties to be integrated in different e-learning systems. Focusing on the computer science field, we present a service-oriented solution for annotating and retrieving the documents and persons involved in this domain, enhancing their mobility inside pervasive e-learning systems. The solution is based on the ACM classification system, which is used in order to develop user competence profiles, to annotate materials, and to retrieve them. The ontology-based retrieval mechanism will investigate not only the matches with the concepts involved in a query, but also with their related concepts inside the ACM classification system; the ranking algorithm will consider the documents content as well as the user competence.

Nowadays, with the great diffusion of mobile technology, and ubiquitous systems, the context has become the ear and the eye of information systems. These systems are more and more based on the usage of Web services. The classical architecture of these services that allows an interoperable interaction between service users and providers does not take in account context adaptation. In this article, we aim to integrate context adaptation within the classical architecture of Web services, adding to it dedicated components that would return to nomadic users a list of Web services that are adapted not only to his profile but also to his context.

International audience; Queries to a database can be made more powerful and user friendly by referring to, cases, either for expressing the request, or for computing,the answer. This requires some similarity-based reasoning facilities. In this paper, we present an implemented information system (applied to a database describing houses to be rent), based on an approach developed in the fuzzy set and possibility theory setting. This provides an unified framework for expressing user's preferences about what he is looking for, for weighting the importance of requirements, for expressing similarity relations, for referring to examples that he likes and/or counter-examples that he dislikes, and for maidng case-based predctions.

This paper provides a preliminary investigation of two potential applications of fuzzy logic in multimedia databases querying. The first one concerns the detection of modifications in semi-structured documents and relies on a graph matching procedure where subparts of the graph describing the structures have different levels of importance. The second illustrates another aspect of the fuzzy logic methodology allowing for a querying by examples process. Examples and counter-examples are assumed to be provided by the user with their levels of representativity; attributes used in their descriptions can be also weighted according to their level of importance.