Eya Ben Ahmed

Group extraction from professional social network using a new semi-supervised hierarchical clustering

Recently, social network has been given much attention. This paper addresses the issue of extraction groups from professional social network and enriches the representation of the user profile and its related groups through building a social network warehousing. Several criteria may be applied to detect groups within professional communities, such as the area of expertise, the job openings proposed by the group, the security of the group, and the time of the group creation. In this paper, we aim to find, extract, and fuse the LinkedIn users. Indeed, we deal with the group extraction of LinkedIn users based on their profiles using our innovative semi-supervised clustering method based on quantitative constraints ranking. The encouraging experimental results carried out on our real professional warehouse show the usefulness of our approach.

Building Con?ict-Aware Profiling Ontology from Data Warehouses

User profiles or user models are crucial in many areas in which it is essential to obtain knowledge about users of software applications such as data warehouse technologies. To enhance the personalized services, group profiles are derived through combining individual user profiles in order to represent group modelling. In this paper, we propose a new representation of group profiles in OLAP context using the ontological modelling. Our main aim is to semantically enrich the representation of group preferences in data warehouses. Our ontology is validated using set of real collected OLAP query logs in stock market area. General Terms Profiling, Data warehouses, Ontology.

SMART: Semantic multidimensional group recommendations

The rising availability of data in the information systems has boosted the challenging problem of queries recommendation, especially in OLAP systems. In this paper, we introduce an innovative 𝓢ℳ𝓐𝓡𝓣 system for semantic multidimensional group recommendations to enhance the querying formulation process. Indeed, we describe the problem of group recommendation and define its semantics through introducing our group profiling ontology. Thus, we infer the analysts’ ongoing behaviors on our ontological concepts using a weighted summation strategy. Based on our ontological representation, we propose a new method for deriving relevant semantic recommendations (i.e., complete and queries fragments). In addition, an optimization technique for selecting the most interesting visualization of recommendations is proposed. Carried out experiments of our 𝓢ℳ𝓐𝓡𝓣 system on real built financial data warehouse highlight encouraging results in terms of precision and recall.

On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies

The decision-making process can be supported by many pioneering technologies such as Data Warehouse (DW), On-Line Analytical Processing (OLAP), and Data Mining (DM). Much research found in literature is aimed at integrating these popular research topics. In this chapter, we focus on discovering cyclic patterns from advanced multi-dimensional context, specially parallel hierarchies where more than one hierarchy is associated to given dimension in respect to several analytical purposes. Thus, we introduce a new framework for cyclic association rules mining from multiple hierarchies. To exemplify our proposal, an illustrative example is provided throughout the article. Finally, we perform intensive experiments on synthetic and real data to emphasize the interest of our approach.

ONTOSSN: Scientific social network ontology

During the past decade, the advent of the social network has offered several platforms that promote communication among users on common spaces. Several efforts were devoted to unify the social network domain, particularly the scientific domain through introducing ontology-based modeling of scientific social network. However, the measurement of the researchers standings within the scientific community is generally absent. To overcome this drawback, we propose, in this paper, a scientific social network ontology which includes definitions of main entities and describes main attributes of : Scientific social network concepts aiming to share common understanding of this domain and to reflect the academic career paths.