The Semantic Web represents a huge information space where an increasing number of datasets, described in RDF, are made available to users and applications. In this context, the data is not constrained by a predefined schema. In RDF datasets, the schema may be incomplete or even missing. While this offers high flexibility in creating data sources, it also makes their use difficult. Several works have addressed the problem of automatic schema discovery for RDF datasets, but existing approaches rely only on the explicit information provided by the data source, which may limit the quality of the results. Indeed, in an RDF data source, an entity is described by explicitly declared properties, but also by implicit properties that can be derived using reasoning rules. These implicit properties are not considered by existing schema discovery approaches. In this work, we propose a first contribution towards a hybrid schema discovery approach capable of exploiting all the semantics of a data source, which is represented not only by the explicitly declared triples, but also by the ones that can be inferred through reasoning. By considering both explicit and implicit properties, the quality of the generated schema is improved. We provide a scalable design of our approach to enable the processing of large RDF data sources while improving the quality of the results. We present some experiments which demonstrate the efficiency of our proposal and the quality of the discovered schema.

Profiles and contexts are the main concepts used by modern applications (e.g. e-commerce and recommender systems) to adapt content delivery services to the users' needs, preferences and environment. Although the definitions of the two terms slightly differ from one application to another, there is a general agreement to distinguish them and use them separately or jointly in a given application. When used jointly, the relationship between the two concepts remains often unclear. This paper aims at providing a personalization model that encompasses profile, context, and a formal relationships between the two. This relationship, called con-textualization, is represented by a set of ranked mappings, automatically extracted from a usage history (log file of user actions). Profile, context and contextualization constitute three structuring elements over which any personalized system should be built. The proposal is supported by a design platform which helps in instantiating profiles and contexts and in generating contextual mappings between them. An instantiation of the meta model is given for an advanced recommender system, called context-aware recommender system (or CARS for short). This instantiation is followed by an experiment highlighting the benefit of contextualization.

Access to relevant information, adapted to user's needs, preferences and environment, is a challenge in many applications running in content delivery platforms, like IPTV, VoD and mobile Video. In order to provide users with personalized content, applications use various techniques such as content recommendation, content filtering, preference-driven queries, etc. These techniques exploit different knowledge organized into profiles and contexts. However, there is not a common understanding of these concepts and there is no clear foundation of what a personalized access model should be. This paper contributes to this concern by providing, through a meta model, a clear distinction between profile and context, and by providing a set of services which constitutes a basement to the definition of a personalized access model (PAM). Our PAM definition allows applications to interoperate in multiple personalization scenarios, including, preference-based recommendation, context-aware content delivery, personalized access to multiple contents, etc. Concepts and services proposed are tightly defined with respect to real applications requirements provided by Alcatel-Lucent.

Understanding data semantics from existing relational databases is important for several applications such as database maintenance and analysis, database re-engineering, data warehouse design or query optimization. We present a tool called DBA Companion, which can be a help to deal with the understanding of existing relational databases. The prototype integrates algorithms dedicated to database analysis. This task rests on data mining techniques, which allow to design efficient algorithms. Emphasis is put on algorithm efficiency to be able to address operational situations, and then discover FDs and INDs satisfied in a database instance. The tool follows a loosely coupled approach with the underlying DBMS for algorithm execution. For instance, IARs are generated in the DBMS, which allows the user to modify them and to reiterate the analysis process from these new relations.

In this article, we deal with the functional and approximate dependency inference problem by pointing out some relationships between relational database theory and formal concept analysis (FCA). More precisely, the notion of functional dependency in database is compared to the notion of implication in FCA. We propose a framework and several algorithms for mining these dependencies from large database relations. The common data centric step of this framework is the discovery of agree sets, which are closed sets with respect to the closure operator for functional dependency. Two approaches for discovering agree sets from database relations are proposed: the former is a database approach based on SQL queries and the latter is a data mining approach based on partitions. Experiments were performed in order to compare the two proposed methods.

The authors propose a framework for a broad class of data mining algorithms for understanding existing databases: functional and approximate dependency inference, minimal key inference, example relation generation and normal form tests. We point out that the common data centric step of these algorithms is the discovery of agree sets. A set-oriented approach for discovering agree sets from database relations based on SQL queries is proposed. Experiments have been performed in order to compare the proposed approach with a data mining approach. We also present a novel way to extract approximate functional dependencies having minimal errors from agree sets.