Faten Atigui

Reducing Multidimensional Data

Our aim is to elaborate a multidimensional database reduction process which will specify aggregated schema applicable over a period of time as well as retains useful data for decision support. Firstly, we describe a multidimensional database schema composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. Each reduced state is defined through reduction operators. Secondly, we describe our experiments and discuss their results. Evaluating our solution implies executing different requests in various contexts: unreduced single fact table, unreduced relational star schema, reduced star schema or reduced snowflake schema. We show that queries are more efficiently calculated within a reduced star schema.

Big Data and Knowledge Management: How to Implement Conceptual Models in NoSQL Systems?

In 2014, Big Data has passed the top of the Gartner Hype Cycle, proving that Big Data technologies and application start to be mature, becoming more realistic about how Big Data can be useful for organizations. NoSQL data stores are becoming widely used to handle Big Data; these databases operate on schema-less data model enabling users to incorporate new data into their applications without using a predefined schema. But, there is still a need for a conceptual model to define how data will be structured in the database. In this paper, we show how to store Big Data within NoSQL systems. For this, we use the Model Driven Architecture (MDA) that provides a framework for models automatic transformation. Starting from a conceptual model that describes a set of complex objects, we propose transformation rules formalized with QVT to generate a column-oriented NoSQL model. To ensure efficient automatic transformation, we use a logical model that limits the impacts related to technical aspects of column-oriented platforms. We provide experiments of our approach using a case study example taken from the health care domain. The results of our experiments show that the proposed logical model can be effectively implemented in different columnoriented systems independently of their specific technical details.

Logical Unified Modeling For NoSQL DataBases

NoSQL data stores are becoming widely used to handle Big Data; these systems operate on schema-less data model enabling users to incorporate new data into their applications without using a predefined schema. But, there is still a need for a conceptual model to define how data will be structured in the database. In this paper, we show how to store Big Data described by conceptual model within NoSQL systems. For this, we use the Model Driven Architecture (MDA) that provides a framework for models automatic transformation. Starting from a conceptual model describing a set of complex objects, we propose transformation rules formalized with QVT to generate NoSQL physical models. To ensure efficient automatic transformation and to limit the impacts related to technical aspects of NoSQL systems, we propose a generic logical model that is compatible with the three types of NoSQL systems (column, document and graph). We provide experiments of our approach using a case study related to the hea lth care field. The results of our experiments show that the proposed logical model can be effectively transformed into different NoSQL physical models independently of their specific details.

MDA-Based Approach for NoSQL Databases Modelling

It is widely accepted today that relational systems are not appropriate to handle Big Data. This has led to a new category of databases commonly known as NoSQL databases that were created in response to the needs for better scalability, higher flexibility and faster data access. These systems have proven their efficiency to store and query Big Data. Unfortunately, only few works have presented approaches to implement conceptual models describing Big Data in NoSQL systems. This paper proposes an automatic MDA-based approach that provides a set of transformations, formalized with the QVT language, to translate UML conceptual models into NoSQL models. In our approach, we build an intermediate logical model compatible with column, document and graph oriented systems. The advantage of using a unified logical model is that this model remains stable, even though the NoSQL system evolves over time which simplifies the transformation process and saves developers efforts and time.

Using OCL for automatically producing multidimensional models and ETL processes

During the last few years, several frameworks have dealt with Data Warehousing (DW) design issues. Most of these frameworks provide partial answers that focus either on multidimensional (MD) modelling or on Extraction-Transformation-Loading (ETL) modelling. Yet, neither the study of unifying both modelling issues nor their automation have been considered thoroughly. To overcome these limits, we suggest a generic unified method that automatically integrates DW and ETL design. The framework is handled within the Model Driven Architecture (MDA). In this paper we present a unified conceptual model that describes both the DW and its ETL process using the constellation model and the Object Constraint Language (OCL). Morevoer, we give a logical model for the ETL workflow and a set of Query/View/Transformation(QVT) mapping rules from the conceptual level to the logical level and then to the physical one. At the end, we describe the implemented prototype architecture.

Modélisation conjointe des données et des processus pour l’implantation de schémas d’entrepôts

Au cours de ces dernières années, plusieurs approches ont abordé la modélisation et le développement des Entrepôts de Données (ED). La plupart de ces approches fournit des solutions partielles qui traitent soit la modélisation multidimensionnelle, soit la modélisation des processus d’Extraction-Transformation-Chargement. Toutefois, peu de travaux ont visé à unifier ces deux problématiques dans un cadre structuré ou à automatiser le processus d’entreposage complet. Afin de pallier ces limites, nous proposons dans ce papier une démarche unifiée et automatique qui intègre la modélisation des ED et des processus ETL. Cette démarche est définie dans le cadre d’une Architecture Dirigée par les Modèles (MDA). Elle permet (i) de formaliser les besoins des décideurs, ensuite (ii) de générer les modèles conceptuel, logique et physique de l’ED et des processus ETL conjoints (iii) ainsi que les codes de création et d’alimentation (ETL) des structures multidimensionnelles. Les règles de transformation entre modèles sont formalisées en Query/View/Transformation. During the last few years, several frameworks have dealt with Data Warehousing (DW) design issues. Most of these frameworks provide partial answers that focus either on multidimensional modelling (MD) or on Extraction-Transformation-Loading modelling (ETL). However, less attention has been given neither to unifying both modelling issues into a single structured framework nor to automating the warehousing process. To overcome these limits, this paper provides a generic unified and automated method that integrates DW and ETL processes design. The framework is handled within the Model Driven Architecture (MDA). It (i) first helps the designer in modelling the decision-maker’s requirements and then (ii) generates the MD model as well as (iii) the logical and the physical models and finally (iv) generates the code. In this approach, the transformation rules are formalized using the Query/View/Transformation (QVT) language.

A data cube design and construction methodology based on OLAP queries

Business Intelligence systems provide an effective solution for multidimensional online computing and analysis from large volumes of data. In the decision making process, analyzed data are typically stored in a set of cubes often heterogeneous. Most of the time, the structure of these cubes is unknown by decision makers. Our goal is to enable decision makers to express their need via a query in natural language. In this paper, we deal with the problem of data cube design and construction according to a user query where the expressed need is dispersed on more than one cube. We propose a cubes design and construction approach based on fusion of cubes containing a part of the users need. We validate our approach via a tool, called “Design-Cubes-Query”, that implements our approach and we show its use through a case study.