Computer Science

Model-driven engineering (MDE) uses models during the application development process. Thus, the MDE is particularly based on model-driven architecture (MDA), which is one of the important variants of the Object Management Group (OMG). MDA aims to generate source code from abstract models through several model transformations between, and inside the different MDA levels: computation independent model (CIM), platform independent model (PIM), and platform specific model (PSM) before code. In this context, several methods and tools were proposed in the literature and in the industry that aim to automatically generate the source code from the MDA levels. However, researchers still meet many constraints—model specifications, transformation automation, and level traceability. In this paper, we present a tool support, the model-driven architecture for web application (MoDAr-WA), that implements our proposed approach, aiming to automate transformations from the highest MDA level (CIM) to th...

Model transformation becomes a very important technique in software engineering as it helps to guarantee traceability between models and develop software applications quickly, In this context, the purpose of this work is an approach that allows generating UML Use Case Diagram (UCD) from Semantic Business Vocabulary and Business Rules (SBVR) which is a standard introduced by OMG that can be used to capture software requirements in structured English. The paper gives a set of rules that map SBVR element into UCD elements. This approach is a part of our works that focus on automating Model Transformations to generate the application code following standards of Model Driven Architecture approach. Particulary we use SBVR standard and UCD to model the Computation Independent Model (CIM) which is the first level of abstraction. The paper describes also an implementation of this approach as an Eclipse plug-in that automates the transformation rules defined using QVT language. In order to well illustrate our approach, we apply it on RentalCarAgency system.

Model Driven Architecture (MDA) and Agile Methods (AM) are two principal domains that are in the way of improvement and evolution in order to facilitate the development of IT projects. However, these areas evolve separately despite the great number of research that focuses on improving project' development techniques. Thus, our proposal aims to provide a method describing how can Agile Methodologies benefits from MDA, and how MDA can automate activities within AM. In this paper, we present a state of the art of existing works that combine a Model Driven Architecture approach and Agile Methodologies. Then we present our analysis of this combination to identify with which Agile methods, the MDA approach is more adequate. We also propose our vision about how to combine MDA with the selected Agile Methodologies and evaluate the strengths and weaknesses of each methodology during its combination with MDA and finally we present a case study of Rental Car Agency.

Manage Testing is one of the most important and difficult phases in the development process. Indeed, it is a decisive step before deploying a product which aims to verify and validate whether the developed product satisfies customer requirements. This step becomes more and more difficult when we face evolutionary system requirement. Several works and approaches were proposed to deal with this issue but they do not describe any approach to well manage test phase in evolutionary software system. It is in this context that this paper proposes a new approach that resolves the problem of managing tests even in a system with evolutionary requirements. As our works are focused on Modeling Driven Development represented by its variant Model Driven Architecture, we will focus in this work on automating test generation from well known models as Model-Based Testing combined with development life cycle to deal with system evolutions. For this first work, we focus on V life cycle since it specifies different types of test needed to well test a system. In order to illustrate concepts used in our approach, we will present their application in a RentalCarAgency applications.

The Model Driven Architecture is a specific variant of Model Driven Engineering that aims to separate different areas of concerns. This architecture is defined by three levels of abstraction, i.e. Computation Independent Model (CIM), Platform Independent Model (PIM) and Platform Specific Model (PSM) that defines the architecture of the generated code. The transition between these levels is realized throw Model Transformations which are the core of MDA. In this paper we will focus on the highest level of abstraction of MDA which is represented by Computation Independent Model and its transformation into the Platform Independent Model. Our approach is based primarily on OMG standards: UML diagrams (Unified Modeling Languages) and SBVR (Semantic Business Vocabulary and Business Rules). We represent the CIM level by an extension of Use Case Diagram to support Data Object elements, and SBVR standard, while after transformation the PIM level is modeled by Business Class Diagram and System Sequence Diagrams. The paper presents also the implementation of our approach which is an eclipse plug-in that allows to automatically transform models from CIM to PIM. We furthermore illustrate our approach with a case study of a car rental agency management application.

In recent years, the growing volume of RDF data requires scalable, efficient, and more robust systems. To meet this need we find NoSQL databases which are distributed, scalable and powerful systems for large data management. In this paper we present an RDF data store scalable and efficient for large RDF data management based on MongoDB, which is a distributed and document-oriented NoSQL database, MongoDB is the most used NoSQL database in the world [1]. We propose a storage schema that stores RDF triples (subject, predicate, object) in JSON documents to ensure an RDF triple index structure based on JSON-LD. For querying data we transform SPARQL queries to MongoDB language queries through the use of meta-models. The results of the experiments obtained indicate that our approach works well compared to a large volume of RDF data, and show that our system outperforms existing distributed RDF data stores based on NoSQL.

Semantic web technologies are increasingly used in different domains. The core technology of the Semantic Web is the RDF standard. Today with the growth of RDF data it requires systems capable of handling these large volumes of data and responding to very complex requests at the join level, Several RDF data processing systems have been proposed, but are not dedicated to handling complex SPARQL queries. in this paper we present a new approach based on model driven engineering for processing complex SPARQL queries using one of the big data processing tools named Hive. We evaluate our system using three datasets from LUBM Benchmark. The results of this evaluation show the performance, and the scalability of our approach, also give very interesting results when it is compared with existing works.

E-learning is a very dynamic field, constantly growing, which refers to an educa-tional content or learning experiences provided or made by means of digital tech-nologies. The development of this area has a direct impact on the quality of edu-cation and cost reduction. E-learning is now dominated by learning management systems such as Moo-dle, to improve the interoperability of these systems; the use of data / resources is a major requirement, this need to push the e-Learning community to seek new approaches to improve this interoperability of systems. On the other hand, se-mantic Web technologies offers standards like RDF allows to describe Web re-sources with their metadata. We propose in this work an approach based on Se-mantic Web technology to improve the interoperability of e-Learning systems like Moodle.

Today, many researches are focused on the use of Big Data technologies for managing large volumes of Semantic Web data. The use of Big Data technologies such as NoSQL database management systems guarantees the scalability and high availability of Web data. The amount of data Web increases day after day and in a way excessive. To fully exploit these data and its metadata. This requires this technology of the Semantic Web to be a scalable and high performance at the level of the storage of these Web data. Many research efforts have been devoted to create and develop a distributed RDF data management system. To achieve the scalability and high performance, these systems are implemented on the basis of the Management technology of Big Data called NoSQL. In this paper, we present a comparative study of existing systems dedicated to managing large volumes of RDF data, which are generally based on NoSQL database management systems.

Semantic web technologies are increasingly used in different domains. The core technology of the Semantic Web is the RDF standard. Today with the growth of RDF data it requires systems capable of handling these large volumes of data and responding to very complex queries at the join level. With the increase in RDF data volumes available, many research efforts have been made to allow for distributed and efficient evaluation of SPARQL queries. In this paper, we propose a new solution based on Apache Spark for massive querying and RDF data. This new system allows the processing of complex SPARQL queries on large volumes of RDF data stored in the Hadoop file system or a NoSQL database management system. Our approach translates the SPARQL query of the user to a Spark script in order to take advantage of the speed of processing and the performance of Spark compared to other existing systems, thanks to an experiment carried out on RDF datasets.

In Web 3.0, semantic data gives machines the ability to understand and process data. Resource Description Framework (RDF) is the liagna franca of Semantic Web. While Big Data handles the problematic of storing and processing massive data, it still does not provide a support for RDF data. In this paper, we present a new Big Data semantic web comprised of a classical Big Data system with a semantic layer. As a proof of concept of our approach, we use Mobile-learning as a case study. The architecture we propose is composed of two main parts: a knowledge server and an adaptation model. The knowledge server allows trainers and business experts to represent their expertise using business rules and ontology to ensure heterogeneous knowledge. Then, in a mobility environment, the knowledge server makes it possible to take into account the constraints of the environment and the user constraints thanks to the RDF exchange format. The adaptation model based on RDF graphs corresponds to combinat...

Internet of Things (IoT) is a set of technologies that aim at fitting together smart devices and applications to build an IoT ecosystem. The target of this kind of ecosystem is to enhance interaction between machines and humans through hardware to software binding while reducing cost and resource consumption. On the application level, IoT ecosystems were implemented by various technologies that all seek better interconnection, monitoring, and controlling of IoT smart devices. Among recent technologies, Microservices, which are a variant of the serviceoriented architecture, are subject to great excitement. In fact, Microservices are an emerging technology built around Microservice paradigm which goal is to offer services with a small granularity, which exactly meets the distributed nature of IoT devices while maintaining a loosely coupled architecture between IoT components among other advantages. Efforts to build Microservice-based IoT platforms sooner emerged to take advantage of the numerous benefits of the Microservice paradigm to build scalable, interoperable, and dynamic ecosystems. The goal of this paper is to list these approaches, classify them and compare them using a Weighted Scoring Model (WSM) method. This involves, studying these platforms, establishing relevant criteria for comparison, assigning weights for each criterion, and finally calculating scores. The obtained results reveal the weaknesses and strengths of each of the studied platforms.

The digital transformation of companies and organizations leads to an evolution of databases towards Big Data. Our work is part of this transformation and concerns more specifically the mechanisms for processing semantic data stored on a Hadoop Big Data or NoSQL platform. Generally, for querying semantic Web data we have to use SPARQL language, and Big Data languages like Apache Spark, are not dedicated to processing this type of data. In this paper, we present a new model engineering approach for analyzing and transforming complex SPARQL queries into Spark scripts. To automate this transformation process, we used the MDE approach that provides a formal framework for the transformation and mapping mechanisms between two languages. From a SPARQL metamodel, we propose transformation rules to generate, in fine, a Spark script intended for a Hadoop / NoSQL platform. We introduce a logic level intermediate schema to limit the impact of Spark releases. An experimentation of the transforma...

Today the Resource Description Framework (RDF) that allows computers to understand and exploit Web data becomes very much in a progressive way, as well as the amount of web data that becomes very large. The storage and efficient management of this large RDF data is a real challenge in front of the classic RDF databases called triplestore. Recently, several researches focus on storing RDF data in triplestores based on NoSQL data management systems like HBase, Cassandra, Accumulo, and Couchbase. The majority of these researches are based on HBase. This NoSQL technology that is intended to handle this phenomenon of data explosion called Big Data, provided benefits like scalability and high availability compared to traditional triplestores. In this paper, we review existing works and systems that use NoSQL databases to store massive RDF data.

Business Intelligence or Decision Support System (DSS) is IT for decision-makers and business leaders. It describes the means, the tools and the methods that make it possible to collect, consolidate, model and restore the data, material or immaterial, of a company to offer a decision aid and to allow a decision-maker to have an overview of the activity being treated. Given the large volume, variety, and data velocity we entered the era of Big Data. And since most of today's BI tools are designed to handle structured data. In our research project, we aim to consolidate a BI system for Big Data. In continuous efforts, this paper is a progress report of our first contribution that aims to apply the techniques of model engineering to propose a universal approach to deal with Big Data is to help decision-makers make decisions.

The era of big data has emerged. The volume of generated data has never been greater. Massive quantities of data are stored on a huge number of servers that are interconnected and share their storage space. Computation methods have been developed to perform computation operations directly on these machines, previously used mainly for storage. Tools such as Hive, Pig, and Spark provide the means for data query and analysis but are not suitable for Semantic Data. For this kind of data, a specialized tool called SPARQL is dedicated to query semantic data represented by the Resource Description Framework or RDF. The aim of our work is to transform a given SPARQL query into a Hive program, a Pig program or a Spark script according to the user's choice. To achieve this goal, we propose a Model-Driven Approach which consists of creating a metamodel for each of these tools, to define a mapping between SPARQL metamodel on one hand and each of the previous Big Data query languages (Pig, Hive, and Spark). The transformation is then performed using Atlas Transformation Language or ATL. We conducted that an experiment on three datasets containing a large volume of distributed RDF data on a powerful server cluster to validate our approach.

Contemporary cities face many challenges: energy, ecological, demographic or economic. To answer this, technological means are implemented in cities through the use of sensors and actuators. These cities are said to be smart. Currently, smart cities are operated by actors who share neither their sensor data nor access to their actuators. This situation is called vertical: each operator deploys its own sensors and actuators and has its own IT infrastructure hosting its applications. This leads to infrastructure redundancy and ad-hoc applications to oversee and control an area of the city. A trend is to move towards a so-called horizontal situation via the use of an open and shared mediation platform. Sensor data and access to the actuators are shared within this type of platform, allowing their sharing between the different actors. The costs of infrastructure and development are then reduced. This work is part of such a context of horizontalization, within an open and shared platform, in which we propose: 1) a layer of abstraction for control and supervision of the city, 2) a competition control mechanism handling conflict cases based on the RDF (Resource Description Framework) semantic Web standard, 3) a coordination mechanism promoting the reuse of actuators using ontology, 4) an implementation of our work by a proof of concept. The abstraction we propose is based on models from reactive systems. They aim to be generic and represent the invariant of the smart city: the physical elements. They allow applications to control and supervise the city. To facilitate the development of applications we standardize the interface of our models. Since these applications may have realtime constraints, especially those that have control objectives, we propose to take advantage of the distributed architecture of this type of platform. Given the sharing of the actuators, we have identified that conflicts can arise between applications. We propose a mechanism of competition control to deal with these cases of conflicts. We have also identified that a coordination mechanism must be offered to applications wishing to perform atomic control operations. Such a mechanism promotes the reuse of the actuators present in the city. Finally, we implemented our proposals around a proof of concept, including several use cases, to demonstrate our work.