Ahlem Nabli

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.

Automatic construction of multidimensional schema from OLAP requirements

Summary form only given. The manual design of data warehouse and data mart schemes can be a tedious, error-prone, and time-consuming task. In addition, it is a highly complex engineering task that calls for methodological support. This paper lays the grounds for an automatic generation approach of multidimensional schemes. It first defines a tabular format for OLAP requirements. Secondly, it presents a set of algebraic operators used to transform automatically the OLAP requirements, specified in the tabular format, to data mart modelled either as star or constellation schemes. Our approach is illustrated with an example.

An Ontology Based Method for Normalisation of Multidimensional Terminology

The data warehouse design raises several problems such as the integration of heterogeneous data sources. In fact, the main difficulty is how interpret automatically the semantic of the heterogeneous and autonomous data. In addition, in information system design, the use of ontology becomes more and more promising; it contributes to avoid semantic and structural ambiguities. This research introduces the concept of decisional ontology as an assistance tool for the specification of analytical requirements. For this, we propose an ontology based method to standardize the multidimensional terminology extracted from several heterogeneous data sources.

SHACUN : semi-supervised hierarchical active clustering based on ranking constraints

Semi-supervised approaches have proven to be efficient in clustering tasks. They allow user input, thus enhancing the quality of the clustering. However, the user intervention is generally limited to integrate boolean constraints in form of must-link and cannot-link constraints between pairs of objects. This paper investigates the issue of satisfying ranked constraints in performing hierarchical clustering.

Two-ETL Phases for Data Warehouse Creation: Design and Implementation

\mathcal{SHACUN}

Building Con?ict-Aware Profiling Ontology from Data Warehouses

is a new introduced method for handling cases when some constraints are more important than others and must be firstly enforced. Carried out experiments on real log files used for decision-maker groupization in data warehouse confirm the soundness of our approach.

FOAF-based clustering of handicraft women using ranked features

Building the ETL process is potentially one of the biggest tasks of building a warehouse. In fact, it is complex, time consuming, and consumes most of data warehouse projects implementation efforts, costs, and resources. Nevertheless, the difference on data structures imposes new requirements on the ETL process implementation and maintenance. What makes these tasks even more challenging is the fact that data continue to grow rapidly and business requirements change over time. In this paper, we propose a method that contains Two-ETL phases, one treats the pre-treatment phase and another deals with the actual ETL. Our method consists on determining the correspondence table, modeling new operations using the Business Process Modeling Notation (BPMN) and implementing these operations with Talend Open Source (TOS). In addition, our method allows the design of ETL process in an earlier stage, which enormously facilitates the implementation of this process. Another advantage of our proposal is the use of the BPMN which allows to cover a deficit of communication that often occurs between the design and implementation of business processes.

On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies

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.

SOIM: Similarity Measures on Ontology Instances Based on Mixed Features

This paper builds upon the BWEC1 (Business for Women in Women of Emerging Country) research project to improve the socio-economic situation of handicraft women. In this project our principal task is to build data warehouse schema from handicraft women social network. For that, we follow a semi-supervised clustering-based methodology. In this paper, we propose the adaptation of a semi-supervised hierarchical clustering based on ranking mixed features for the FAOF ontology. This later serves as perfect input data for clustering. The main contribution is to use ontology-based similarity measures that combine numerical and nominal variables along different dimensions (instances, attributes, and relation-ships) and to provide a performable clustering algorithm based on ranking features. The evaluation of the used clustering methods in the context of the project emphasizes it effectiveness to generate valid clusters which can be successfully used for extending the data warehouse schema.

A New Schema for Securing Data Warehouse Hosted in the Cloud

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.