Faiza Ghozzi

Contraintes pour modèle et langage multidimensionnels

This paper defines a constraint-based model dedicated to multidimensional databases. The model we define represents data through a constellation of facts (subjects of analyse) associated to dimensions (axis of analyse), which are possibly shared. Each dimension is organised according to several hierarchies (views of analyse) integrating several levels of data granularity. In order to insure data consistency, we introduce 5 semantic constraints (exclusion, inclusion, partition, simultaneity, totality) which can be intra-dimension or inter-dimensions; the intra-dimension constraints allow the expression of constraints between hierarchies within a same dimension whereas the inter-dimensions constraints focus on hierarchies of distinct dimensions. We also study repercussions of these constraints on multidimensional manipulations and we provide extensions of the multidimensional operators.

BigDimETL: ETL for multidimensional Big Data

With the broad range of data available on the World Wide Web and the increasing use of social media such as Facebook, Twitter, YouTube, etc. a “Big Data” notion has emerged. This latter has become an important aspect in nowadays business since it is full of important knowledge that is crucial for effective decision making. However, this kind of data brings with it new problems and challenges for the Decision Support System (DSS) that must be addressed. In this paper, we propose a new approach called BigDimETL (Big Dimensional ETL) that deals with ETL (Extract-Transform-Load) development process. Our approach focuses on integrating Big Data taking into account the MultiDimensional Structure (MDS) through a MapReduce paradigm.

ETL Transformation Algorithm for Facebook Opinion Data

Considered as a rich source of information, social networking sites have been created lot of buzz because people share and discuss their opinions freely. Sentiment analysis is used for knowing voice or response of crowd for products, services, organizations, individuals, events, etc. Due to their importance, people opinions are analyzed in several domains including information retrieval, semantic web, text mining. These researches define new classification techniques to assign positive or negative opinion. Decisional systems like WeBhouse, known by their data-consuming must be enriched by this kind of pertinent opinions to give better help to decision makers. Nevertheless, cleaning and transformation processes recognized by several approaches as a key of WeBhouse development, don’t deal with sentiment analysis. To fulfill this gap, we propose a new analysis algorithm which determines user’s sentiment score of a post shared on the social network Facebook. This algorithm analyzes user’s opinion depending on opinion terms and emoticons included in his comments. This algorithm is integrated in transformation process of ETL approach.

Querying Heterogeneous Document Stores

NoSQL document stores offer support to store documents described using various structures. Hence, the user has to formulate queries using the possible representations of the desired information from different schemas. In this paper, we propose a novel approach that enables querying operators over a collection of documents with structural heterogeneity. Our work introduces an automatic query rewriting mechanism based on combinations of elementary operators: project, restrict and aggregate. We generate a custom dictionary that tracks all representations for attributes used in the documents. Finally, we discuss the results of our approach with a series of experiments.

BigDimETL with NoSQL Database

Abstract In the last decade, we have witnessed an explosion of data volume available on the Web. This is due to the rapid technological advances with the availability of smart devices and social networks such as Twitter, Facebook, Instagram, etc. Hence, the concept of Big Data was created to face this constant increase. In this context, many domains should take in consideration this growth of data, especially, the Business Intelligence (BI) domain. Where, it is full of important knowledge that is crucial for effective decision making. However, new problems and challenges have appeared for the Decision Support System that must be addressed. Accordingly, the purpose of this paper is to adapt Extract-Transform-Load (ETL) processes with Big Data technologies, in order to support decision-making and knowledge discovery. In this paper, we propose a new approach called Big Dimensional ETL (BigDimETL) dealing with ETL development process and taking into account the Multidimensional structure. In addition, in order to accelerate data handling we used the MapReduce paradigm and Hbase as a distributed storage mechanism that provides data warehousing capabilities. Experimental results show that our ETL operation adaptation can perform well especially with Join operation.

ETL4Social-Data: Modeling Approach for Topic Hierarchy.

Transforming social media data into meaningful and useful information to enable more effective decisionmaking is nowadays a hot topic for Social Business Intelligence (SBI) systems. Integrating such data into Social Data Warehouse (SDW) is in charge of ETL (Extraction, Transformation and Loading) which are the typical processes recognized as a complex combination of operations and technologies that consumes a significant portion of the DW development efforts. These processes become more complex when we consider the unstructured social sources. For that, we propose an ETL4Social modeling approach that designs ETL processes suitable to social data characteristics. This approach offers specific models to social ETL operations that help ETL designer to integrate data. A key role in the analysis of textual data is also played by topics, meant as specific concepts of interest within a subject area. In this paper, we mainly insist on emerging topic discovering models from textual media clips. The proposed models are instantiated through Twitter case study. ETL4Social is considered a standard-based modeling approach using Business Process Modeling and Notation (BPMN). ETL Operations models are validated based on ETL4Social metamodel, which is an extension of BPMN meta-model.

POMap: An Effective Pairwise Ontology Matching System.

The identification of alignments between heterogeneous ontologies is one of the main research issues in the semantic web. The manual matching of the ontologies is a complex, time consuming and an error prone task. Therefore, ontology matching systems aims to automate this process. Usually, these systems perform the matching process by combining element and structural level matchers. Selecting the optimal string similarity measure associated with its threshold is an important issue in order to enhance the effectiveness of the element level matcher, which in turn will improve the whole ontology system results. In this paper, we present POMap, an ontology matching system based on a syntactic study covering element and structural levels. For the element level matcher we have adopted the best configuration based on the analysis of the performances of many string similarity measures associated with their thresholds. For the structural level, we have performed a syntactic study on both subclasses and siblings in order to infer the structural similarity. Our proposed matching system is validated and evaluated on the Anatomy, the Conference and the Large Biomedical tracks provided by the benchmark of OAEI 2016 ontology matching campaign.