Skip to main content

Faten Atigui

Followers

6

Following

1

Public Views

Haitham El-Ghareeb

Mansoura University

Cairo University

Kansas State University

Armando Marques-Guedes

UNL - New University of Lisbon

Dr. Gulshan Shrivastava

Galgotias University,Greater Noida

Yale University

Universidade de Coimbra

Viacheslav Kuleshov

Stockholm University

Journal of Computer Science IJCSIS

Ataturk University

Interests

Uploads

Papers by Faten Atigui

Semantic-Based Approach for Cyber-Physical Cascading Effects Within Healthcare Infrastructures

IEEE Access

This paper presents a framework for integrated cyber-physical security propagation study within c... more This paper presents a framework for integrated cyber-physical security propagation study within critical infrastructures (hospitals use case). The framework includes an ontology for the semantic modeling of cyber-physical security. The impact propagation approach relies on propagation rules inferring the cascading effects of security incidents occurring in hospitals. An impact score module allows evaluating impacts' severity, considering implemented protection measures. This work is part of the European project SAFECARE, which provides a set of tests and demonstration sessions in partnership with 3 hospitals in Europe. During these tests, we measured effectiveness and efficiency metrics, as well as end users' satisfaction feedback. Experiments performed on real attack scenarios, show high effectiveness rates and a common agreement from end-users about the added value of the solution to enhance risks analysis practice and increase hospitals mitigation strategies efficiency. INDEX TERMS Critical infrastructure, cyber-physical security, impact propagation, ontologies, semantic modeling.

UMLtoNoSQL: Automatic Transformation of Conceptual Schema to NoSQL Databases

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017

An embedding driven approach to automatically detect identifiers and references in document stores

Data & Knowledge Engineering, 2022

IRIS-DS: A New Approach for Identifiers and References Discovery in Document Stores

Proceedings of the Annual Hawaii International Conference on System Sciences, 2021

NoSQL stores offer a new cost-effective and schema-free system. Although it is widely accepted to... more NoSQL stores offer a new cost-effective and schema-free system. Although it is widely accepted today, Business Intelligence & Analytics (BI&A) remains associated with relational databases. Exploiting schema-free data for analytical purposes is issuing a challenge since it requires reviewing all the BI&A phases, particularly the Extract-Transform-Load (ETL) process, to fit big data sources as document stores. In the ETL process, the join of several collections, with a lack of explicitly known join fields, is a significant challenge. Detecting these fields manually is time and effort consuming, and even infeasible in large-scale datasets. In this paper, we study the problem of discovering join fields automatically, and introduce an algorithm to detect both identifiers and references on several document stores. The modus operandi of our approach underscores two core stages: (i) discovery of identifier candidates; and (ii) identifying candidate pairs of identifier and reference fields. We use scoring features and pruning rules based on both syntactic and semantic aspects to efficiently discover true candidates from a huge number of initial ones. Finally, we report our experimental findings that show very promising results.

11. Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Cyber-Physical Threat Intelligence for Critical Infrastructures Security: A Guide to Integrated Cyber-Physical Protection of Modern Critical Infrastructures, 2020

Modelling Cyber-Physical Security in Healthcare Systems

Lecture Notes in Business Information Processing, 2021

Business Intelligence and Analytics: On-demand ETL over Document Stores

Research Challenges in Information Science, 2020

For many decades, Business Intelligence and Analytics (BI&A) has been associated with relatio... more For many decades, Business Intelligence and Analytics (BI&A) has been associated with relational databases. In the era of big data and NoSQL stores, it is important to provide approaches and systems capable of analyzing this type of data for decision-making. In this paper, we present a new BI&A approach that both: (i) extracts, transforms and loads the required data for OLAP analysis (on-demand ETL) from document stores, and (ii) provides the models and the systems required for suitable OLAP analysis. We focus here, on the on-demand ETL stage where, unlike existing works, we consider the dispersion of data over two or more collections.

Towards Automatic Generation of NoSQL Document-Oriented Models

The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, so... more The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, social network, etc.) has resulted in an exponential increase in data volumes, now measured in Petabytes. Managing these volumes of data has become a problem that relational databases are no longer able to handle because of the acidity properties. In response to this scaling up, new concepts have emerged such as NoSQL. In this paper, we show how to design and apply transformation rules to migrate from an SQL relational database to a Big Data solution within NoSQL. For this, we use the Model Driven Architecture (MDA) and the transformation languages like as MOF 2.0 QVT (Meta-Object Facility 2.0 Query-View-Transformation) and Acceleo which define the meta-models for the development of transformation model. The transformation rules defined in this work can generate, from the class diagram, a CQL code for creation column-oriented NoSQL database.

Processus de transformation MDA d'un schéma conceptuel de données en un schéma logique NoSQL

La transformation digitale des entreprises et plus largement celle de la societe, entraine une ev... more La transformation digitale des entreprises et plus largement celle de la societe, entraine une evolution des bases de donnees vers le Big Data. Nos travaux s'inscrivent dans cette mutation et concernent plus particulierement les mecanismes d'implantation d'une base de donnees sur une plateforme NoSQL. Pour automatiser ce processus d'implantation, nous avons utilise l'architecture MDA qui offre un cadre formalise aux mecanismes de transformation des schemas. A partir d'un schema conceptuel decrivant une base d'objets complexes, nous proposons des regles de derivation pour generer, in fine, un schema d'implantation destine a une plateforme NoSQL orientee colonnes. Nous introduisons un schema intermediaire de niveau logique afin de limiter les impacts lies aux evolutions techniques des plateformes NoSQL. Une experimentation du processus de transformation a ete realisee sur une application medicale.

Modeling Framework for NoSQL Systems

International audienc

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

Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2016

In 2014, Big Data has passed the top of the Gartner Hype Cycle, proving that Big Data technologie... more 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.

MDA-Based Approach for NoSQL Databases Modelling

Big Data Analytics and Knowledge Discovery, 2017

It is widely accepted today that relational systems are not appropriate to handle Big Data. This ... more 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. Keywords: UML Á NoSQL Á Big data Á MDA Á QVT Á Models transformation 2 Research Problem and Related Work Big Data applications developers have to deal with the question: how to store Big Data in NoSQL systems? To address this problem, existing solutions propose to model Big Data, and then define mapping rules towards the physical level.

SafecareOnto: A Cyber-Physical Security Ontology for Healthcare Systems

Lecture Notes in Computer Science, 2021

Mod�lisation conjointe des donn�es et des processus pour l�implantation de sch�mas d�entrep�ts

Journal of Decision Systems, 2012

Mod`ele unifi'e pour la transformation des sch'emas en constellation

Au cours de ces dernières années, plusieurs approches ont abordé la modélisation et le développem... more 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 multidimensionnelles, soit la modélisation des processus d'Extraction-Transformation-Loading (ETL). Toutefois, peu de travaux ont visé à unifier ces deux problématiques dans un cadre structuré ou à automatiser le processus d'entreposage. Afin de pallier ces limites, nous proposons dans ce papier une démarche unifiée et semi-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 transformations entre modèles sont formalisées en Query/View/Transformation.

Facilitate Effective Decision-Making by Warehousing Reduced Data

International Journal of Decision Support System Technology, 2015

The authors' aim is to provide a solution for multidimensional data warehouse's reduction... more The authors' aim is to provide a solution for multidimensional data warehouse's reduction based on analysts' needs which will specify aggregated schema applicable over a period of time as well as retain only useful data for decision support. Firstly, they describe a conceptual modeling for multidimensional data warehouse. A multidimensional data warehouse's schema is composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. The derivation between states is carried out through combination of reduction operators. Secondly, they present a meta-model which allows managing different states of multidimensional data warehouse. The definition of reduced and unreduced multidimensional data warehouse schema can be carried out by instantiating the meta-model. Finally, they describe their experimental assessments and discuss their results. Evaluating their solution implies executing different queries in various contexts:...

Using OCL for Automatically Producing Multidimensional Models and ETL Processes

Lecture Notes in Computer Science, 2012

During the last few years, several frameworks have dealt with Data Warehousing (DW) design issues... more 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.

Reducing Multidimensional Data

Lecture Notes in Computer Science, 2014

Our aim is to elaborate a multidimensional database reduction process which will specify aggregat... more 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.

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

Journal of Decision Systems, 2012

Au cours de ces dernières années, plusieurs approches ont abordé la modélisation et le développem... more 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

ModelDrivenGuide: An Approach for Implementing NoSQL Schemas

Lecture Notes in Computer Science, 2020

With data evolution in terms of volume, variety and velocity, Information Systems (IS) administra... more With data evolution in terms of volume, variety and velocity, Information Systems (IS) administrators need to find the best solution to store and manipulate data with respect to their requirements. So far, existing approaches provide rules to transform a source model to a target model, but none of them propose a method to lead the choice of the most suitable solution. ModelDrivenGuide suggests a model transformation approach that focuses on proposing the different relevant solutions to the case of study. It is based on a common meta-model for the 5 families (Relational & NoSQL) and a generation heuristic. Our approach is validated using the TPC-C benchmark.

Semantic-Based Approach for Cyber-Physical Cascading Effects Within Healthcare Infrastructures

IEEE Access

This paper presents a framework for integrated cyber-physical security propagation study within c... more This paper presents a framework for integrated cyber-physical security propagation study within critical infrastructures (hospitals use case). The framework includes an ontology for the semantic modeling of cyber-physical security. The impact propagation approach relies on propagation rules inferring the cascading effects of security incidents occurring in hospitals. An impact score module allows evaluating impacts' severity, considering implemented protection measures. This work is part of the European project SAFECARE, which provides a set of tests and demonstration sessions in partnership with 3 hospitals in Europe. During these tests, we measured effectiveness and efficiency metrics, as well as end users' satisfaction feedback. Experiments performed on real attack scenarios, show high effectiveness rates and a common agreement from end-users about the added value of the solution to enhance risks analysis practice and increase hospitals mitigation strategies efficiency. INDEX TERMS Critical infrastructure, cyber-physical security, impact propagation, ontologies, semantic modeling.

UMLtoNoSQL: Automatic Transformation of Conceptual Schema to NoSQL Databases

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017

An embedding driven approach to automatically detect identifiers and references in document stores

Data & Knowledge Engineering, 2022

IRIS-DS: A New Approach for Identifiers and References Discovery in Document Stores

Proceedings of the Annual Hawaii International Conference on System Sciences, 2021

NoSQL stores offer a new cost-effective and schema-free system. Although it is widely accepted to... more NoSQL stores offer a new cost-effective and schema-free system. Although it is widely accepted today, Business Intelligence & Analytics (BI&A) remains associated with relational databases. Exploiting schema-free data for analytical purposes is issuing a challenge since it requires reviewing all the BI&A phases, particularly the Extract-Transform-Load (ETL) process, to fit big data sources as document stores. In the ETL process, the join of several collections, with a lack of explicitly known join fields, is a significant challenge. Detecting these fields manually is time and effort consuming, and even infeasible in large-scale datasets. In this paper, we study the problem of discovering join fields automatically, and introduce an algorithm to detect both identifiers and references on several document stores. The modus operandi of our approach underscores two core stages: (i) discovery of identifier candidates; and (ii) identifying candidate pairs of identifier and reference fields. We use scoring features and pruning rules based on both syntactic and semantic aspects to efficiently discover true candidates from a huge number of initial ones. Finally, we report our experimental findings that show very promising results.

11. Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Cyber-Physical Threat Intelligence for Critical Infrastructures Security: A Guide to Integrated Cyber-Physical Protection of Modern Critical Infrastructures, 2020

Modelling Cyber-Physical Security in Healthcare Systems

Lecture Notes in Business Information Processing, 2021

Business Intelligence and Analytics: On-demand ETL over Document Stores

Research Challenges in Information Science, 2020

For many decades, Business Intelligence and Analytics (BI&A) has been associated with relatio... more For many decades, Business Intelligence and Analytics (BI&A) has been associated with relational databases. In the era of big data and NoSQL stores, it is important to provide approaches and systems capable of analyzing this type of data for decision-making. In this paper, we present a new BI&A approach that both: (i) extracts, transforms and loads the required data for OLAP analysis (on-demand ETL) from document stores, and (ii) provides the models and the systems required for suitable OLAP analysis. We focus here, on the on-demand ETL stage where, unlike existing works, we consider the dispersion of data over two or more collections.

Towards Automatic Generation of NoSQL Document-Oriented Models

The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, so... more The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, social network, etc.) has resulted in an exponential increase in data volumes, now measured in Petabytes. Managing these volumes of data has become a problem that relational databases are no longer able to handle because of the acidity properties. In response to this scaling up, new concepts have emerged such as NoSQL. In this paper, we show how to design and apply transformation rules to migrate from an SQL relational database to a Big Data solution within NoSQL. For this, we use the Model Driven Architecture (MDA) and the transformation languages like as MOF 2.0 QVT (Meta-Object Facility 2.0 Query-View-Transformation) and Acceleo which define the meta-models for the development of transformation model. The transformation rules defined in this work can generate, from the class diagram, a CQL code for creation column-oriented NoSQL database.

Processus de transformation MDA d'un schéma conceptuel de données en un schéma logique NoSQL

La transformation digitale des entreprises et plus largement celle de la societe, entraine une ev... more La transformation digitale des entreprises et plus largement celle de la societe, entraine une evolution des bases de donnees vers le Big Data. Nos travaux s'inscrivent dans cette mutation et concernent plus particulierement les mecanismes d'implantation d'une base de donnees sur une plateforme NoSQL. Pour automatiser ce processus d'implantation, nous avons utilise l'architecture MDA qui offre un cadre formalise aux mecanismes de transformation des schemas. A partir d'un schema conceptuel decrivant une base d'objets complexes, nous proposons des regles de derivation pour generer, in fine, un schema d'implantation destine a une plateforme NoSQL orientee colonnes. Nous introduisons un schema intermediaire de niveau logique afin de limiter les impacts lies aux evolutions techniques des plateformes NoSQL. Une experimentation du processus de transformation a ete realisee sur une application medicale.

Modeling Framework for NoSQL Systems

International audienc

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

Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2016

In 2014, Big Data has passed the top of the Gartner Hype Cycle, proving that Big Data technologie... more 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.

MDA-Based Approach for NoSQL Databases Modelling

Big Data Analytics and Knowledge Discovery, 2017

It is widely accepted today that relational systems are not appropriate to handle Big Data. This ... more 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. Keywords: UML Á NoSQL Á Big data Á MDA Á QVT Á Models transformation 2 Research Problem and Related Work Big Data applications developers have to deal with the question: how to store Big Data in NoSQL systems? To address this problem, existing solutions propose to model Big Data, and then define mapping rules towards the physical level.

SafecareOnto: A Cyber-Physical Security Ontology for Healthcare Systems

Lecture Notes in Computer Science, 2021

Mod�lisation conjointe des donn�es et des processus pour l�implantation de sch�mas d�entrep�ts

Journal of Decision Systems, 2012

Mod`ele unifi'e pour la transformation des sch'emas en constellation

Au cours de ces dernières années, plusieurs approches ont abordé la modélisation et le développem... more 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 multidimensionnelles, soit la modélisation des processus d'Extraction-Transformation-Loading (ETL). Toutefois, peu de travaux ont visé à unifier ces deux problématiques dans un cadre structuré ou à automatiser le processus d'entreposage. Afin de pallier ces limites, nous proposons dans ce papier une démarche unifiée et semi-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 transformations entre modèles sont formalisées en Query/View/Transformation.

Facilitate Effective Decision-Making by Warehousing Reduced Data

International Journal of Decision Support System Technology, 2015

The authors' aim is to provide a solution for multidimensional data warehouse's reduction... more The authors' aim is to provide a solution for multidimensional data warehouse's reduction based on analysts' needs which will specify aggregated schema applicable over a period of time as well as retain only useful data for decision support. Firstly, they describe a conceptual modeling for multidimensional data warehouse. A multidimensional data warehouse's schema is composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. The derivation between states is carried out through combination of reduction operators. Secondly, they present a meta-model which allows managing different states of multidimensional data warehouse. The definition of reduced and unreduced multidimensional data warehouse schema can be carried out by instantiating the meta-model. Finally, they describe their experimental assessments and discuss their results. Evaluating their solution implies executing different queries in various contexts:...

Using OCL for Automatically Producing Multidimensional Models and ETL Processes

Lecture Notes in Computer Science, 2012

During the last few years, several frameworks have dealt with Data Warehousing (DW) design issues... more 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.

Reducing Multidimensional Data

Lecture Notes in Computer Science, 2014

Our aim is to elaborate a multidimensional database reduction process which will specify aggregat... more 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.

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

Journal of Decision Systems, 2012

Au cours de ces dernières années, plusieurs approches ont abordé la modélisation et le développem... more 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

ModelDrivenGuide: An Approach for Implementing NoSQL Schemas

Lecture Notes in Computer Science, 2020

With data evolution in terms of volume, variety and velocity, Information Systems (IS) administra... more With data evolution in terms of volume, variety and velocity, Information Systems (IS) administrators need to find the best solution to store and manipulate data with respect to their requirements. So far, existing approaches provide rules to transform a source model to a target model, but none of them propose a method to lead the choice of the most suitable solution. ModelDrivenGuide suggests a model transformation approach that focuses on proposing the different relevant solutions to the case of study. It is based on a common meta-model for the 5 families (Relational & NoSQL) and a generation heuristic. Our approach is validated using the TPC-C benchmark.