A Formal Model of Information Retrieval Based on User Sensitivities

AbstractSearch engines are a very important web applications used by millions of users around the world on a daily basis to search the Web. Finding relevant information in this growing space is challenging, and is complicated by the diversity and needs of the community of Web users. Indeed, the Web is one, but the needs of users are multiple and different. Thus, information relevancy is not only related to the formulated query, but also to the user who is formulating this query. For example, user sensitivities may enhance information relevancy. In this paper, we are proposing to derive a formal model of user sensitivities integration into search engines

Integrating ontological knowledge in sequential pattern mining process and their application to web personalization

La fouille de données vise à extraire des connaissances à partir d'un grand volume de données. Lorsque les associations et l'ordre chronologique d'apparition des items sont recherchés, les connaissances extraites sont appelées motifs séquentiels. Les travaux de recherche existants ont porté principalement sur l'étude de motifs séquentiels composés d'objets et dans un certain nombre de cas, de catégories d'objets (concepts). Alors que les motifs d'objets sont trop spécifiques, et de ce fait peuvent être peu fréquents, les motifs de concepts ont divers niveaux d'abstraction et risquent d'être moins précis. La prise en compte d'une ontologie du domaine dans le processus de fouille de données permet de découvrir des motifs plus compacts et plus pertinents qu'en l'absence d'une telle source de connaissance. En outre, les objets peuvent non seulement être décrits par les concepts auxquels ils se rattachent mais aussi par les liens sémantiques qui existent entre concepts. Cependant, les approches de fouille existantes restent restrictives par rapport aux modes d'expression offerts par une ontologie. La contribution de ce travail est de définir la syntaxe et la sémantique d'un langage de motifs qui prennent en considération les connaissances incorporées dans une ontologie lors de la fouille de motifs séquentiels. Ce langage offre un ensemble de primitives pour la description et la manipulation de motifs. La méthode de fouille sous-jacente procède au parcours de l'espace de motifs par niveau en se basant sur un ensemble de primitives de navigation. Ces primitives tiennent compte de la relation de généralisation/spécialisation qui existe entre les concepts (et les relations) des motifs. Afin de valider notre approche et analyser la performance et la mise à l'échelle de l'algorithme proposé, nous avons développé la plateforme OntoMiner. Tout au long de la thèse, le potentiel de notre approche de fouille a été illustré à travers un cas de recommandation Web. Il ressort que l'inclusion des concepts et des relations dans le processus de fouille permet d'avoir des motifs plus pertinents et de meilleures recommandations que les approches classiques de fouille de motifs séquentiels ou de recommandationData mining aims at extracting knowledge patterns classes or exceptions from a large set of data. When both associations and temporal order between items are sought, the discovered knowledge are called sequential patterns. Existing studies were conducted mainly on sequential patterns involving objects and in·some cases object categories. While patterns based on objects are too specific, non frequent, patterns based on categories (concepts) may have different levels of abstraction and be possibly less precise. Taking into account a given domain ontology during a data mining process allows the discovery of more compact and relevant patterns than in case of the absence of such source of knowledge. Moreover, objects may be not only expressed by the concepts they are attached to, but also by the semantic links that hold between concepts. However, related studies that exploited domain knowledge are restrictive with regard to the expressive power offered by ontology. Our contribution consists to define the syntax and the semantics of a pattern language which exploits knowledge embedded in an ontology during the process of mining sequential patterns. The language offers a set of primitives for pattern description and manipulation. Our data mining technique explores the pattern space level by level using a set of navigation primitives which take into account the generalization/spécialization links that hold between concepts (and relationships) contained in patterns at different abstraction levels. ln order to validate our approach and analyze the performance and scalability of the proposed algorithm, we developed the OntoMiner plateform. Throughout this thesis, the potential of our mining approach was illustrated with an example of Web recommendation. We came to the conclusion that taking into account concepts relationships of an ontology during the process of data mining allows the generation of more relevant patterns and leads to better recommendations than conventional approaches for sequential pattern mining or recommendation making

Intégration des connaissances ontologiques dans la fouille de motifs séquentiels avec application à la personnalisation web

Data mining aims at extracting knowledge from large sets of data such as association rules, clusters and patterns. When both associations and temporal order between items are sought, the discovered knowledge are called sequential patterns. Existing studies were conducted mainly on sequential patterns involving objects and in some cases object categories. While patterns based on objects are too specific, non frequent patterns based on categories (concepts) may have different levels of abstraction and be possibly less precise. Taking into account a given domain ontology during a data mining process allows the discovery of more compact and relevant patterns than in case of the absence of such source of knowledge. Moreover, objects may not be only expressed by the concepts they are attached to, but also by the semantic links that hold between concepts. However, related studies that exploited domain knowledge are restrictive with regard to the expressive power offered by ontology. Our contribution consists to define the syntax and the semantics of a pattern lan- guage which exploits knowledge embedded in an ontology during the process of mining sequential patterns. The language offers a set of primitives for pattern description and manipulation. Our data mining technique explores the pattern space level by level using a set of navigation primitives which take into account the generalization/spécialization links that hold between concepts (and relationships) contained in patterns at different abstraction levels. In order to validate our approach and analyze the performance and scalability of the proposed algorithm, we developed the OntoMiner plateform. Throughout this thesis, the potential of our mining approach was illustrated with an ex- ample of Web recommendation. We came to the conclusion that taking into account con- cepts and relationships of an ontology during the process of data mining allows the dis- covery of more relevant patterns and leads to better recommendations than those found without using background knowledge.La fouille de données vise à extraire des connaissances à partir d'un grand volume de données. Lorsque les associations et l'ordre chronologique d'apparition des items sont recherchés, les connaissances extraites sont appelées motifs séquentiels. Les travaux de recherche existants ont porté principalement sur l'étude de motifs séquentiels composés d'objets et dans un certain nombre de cas, de catégories d'objets (concepts). Alors que les motifs d'objets sont trop spécifiques, et de ce fait peuvent être peu fréquents, les motifs de concepts ont divers niveaux d'abstraction et risquent d'être moins précis. La prise en compte d'une ontologie du domaine dans le processus de fouille de données permet de découvrir des motifs plus compacts et plus pertinents qu'en l'absence d'une telle source de connaissance. En outre, les objets peuvent non seulement être décrits par les concepts auxquels ils se rattachent mais aussi par les liens sémantiques qui existent entre concepts. Cependant, les approches de fouille existantes restent restrictives par rapport aux modes d'expression offerts par une ontologie. La contribution de ce travail est de définir la syntaxe et la sémantique d'un langage de motifs qui prend en considération les connaissances incorporées dans une ontologie lors de la fouille de motifs séquentiels. Ce langage offre un ensemble de primitives pour la description et la manipulation de motifs. La méthode de fouille sous-jacente procède au parcours de l'espace de motifs par niveau en se basant sur un ensemble de primitives de navigation. Ces primitives tiennent compte de la relation de généralisation/spécialisation qui existe entre les concepts (et les relations) des motifs. Afin de valider notre approche et analyser la performance et la mise à l'échelle de l'algorithme proposé, nous avons développé la plateforme OntoMiner. Tout au long de la thèse, le potentiel de notre approche de fouille a été illustré à travers un cas de recom- mandation Web. Il ressort que l'inclusion des concepts et des relations dans le processus de fouille permet d'avoir des motifs plus pertinents et de meilleures recommandations que les approches classiques de fouille de motifs séquentiels ou de recommandation

Toward service aggregation for edge computing

Interoperability is one of crucial Internet-related research domains. Today, there is a shift in the architecture of the Internet and the traditional communication model; the human part in machine communication is blurring into a more sophisticated thing-to-thing communication model. In this model things search for other things and provide collaboration-base services, thusly leading to more complex interaction issues. Especially, interoperability must transcend the use of protocols and include semantic to make the different building blocks of the Internet of Things (IoT) work together and exploit the maximum of it. Hence, we propose a multilayer model for IoT infrastructure to abstract the data sources infrastructure, to define filtering and formatting mechanisms, and to present pertinent data in the form of simple unitary or aggregation of multiple services

An Architectural Model for Fog Computing

The adoption of the Internet of Things raises many challenges. A variety of its applications require widespread distribution and high mobility support. In addition to low latency and real time services. To meet these challenges, the Fog Computing is arguably a suitable solution to leverage the Internet of Things with such requirements. Indeed, we believe that the nearness of Fog nodes to the edge of the network provides an environment for critical preemptive and proactive applications and services (e.g., predicting natural disasters). Thus, this paper proposes an architectural model for Fog Computing. First, it presents a middleware to abstract the underlying devices and to unify the sensed data. Second, it describes an Operational Layer intended for service presentation, management and transformation. An environment embracing such model will provide means for early data analysis, hence low latency and real time responses. In addition, to providing an ecosystem for direct collaboration between services leading to more sophisticated applications. A flood warning system exemplifies a use case scenario to illustrate the potential adaption and application of the presented model

HEAD Metamodel: Hierarchical, Extensible, Advanced, and Dynamic Access Control Metamodel for Dynamic and Heterogeneous Structures

The substantial advancements in information technologies have brought unprecedented concepts and challenges to provide solutions and integrate advanced and self-ruling systems in critical and heterogeneous structures. The new generation of networking environments (e.g., the Internet of Things (IoT), cloud computing, etc.) are dynamic and ever-evolving environments. They are composed of various private and public networks, where all resources are distributed and accessed from everywhere. Protecting resources by controlling access to them is a complicated task, especially with the presence of cybercriminals and cyberattacks. What makes this reality also challenging is the diversity and the heterogeneity of access control (AC) models, which are implemented and integrated with a countless number of information systems. The evolution of ubiquitous computing, especially the concept of Industry 4.0 and IoT applications, imposes the need to enhance AC methods since the traditional methods are not able to answer the increasing demand for privacy and security standards. To address this issue, we propose a Hierarchical, Extensible, Advanced, and Dynamic (HEAD) AC metamodel for dynamic and heterogeneous structures that is able to encompass the heterogeneity of the existing AC models. Various AC models can be derived, and different static and dynamic AC policies can be generated using its components. We use Eclipse (xtext) to define the grammar of our AC metamodel. We illustrate our approach with several successful instantiations for various models and hybrid models. Additionally, we provide some examples to show how some of the derived models can be implemented to generate AC policies