Mega-modeling of complex, distributed, heterogeneous CPS systems

Model-Driven Design (MDD) has proven to be a powerful technology to address the development of increasingly complex embedded systems. Beyond complexity itself, challenges come from the need to deal with parallelism and heterogeneity. System design must target different execution platforms with different OSs and HW resources, even bare-metal, support local and distributed systems, and integrate on top of these heterogeneous platforms multiple functional component coming from different sources (developed from scratch, legacy code and third-party code), with different behaviors operating under different models of computation and communication. Additionally, system optimization to improve performance, power consumption, cost, etc. requires analyzing huge lists of possible design solutions. Addressing these challenges require flexible design technologies able to support from a single-source model its architectural mapping to different computing resources, of different kind and in different platforms. Traditional MDD methods and tools typically rely on fixed elements, which makes difficult their integration under this variability. For example, it is unlikely to integrate in the same system legacy code with a third-party component. Usually some re-coding is required to enable such interconnection. This paper proposes a UML/MARTE system modeling methodology able to address the challenges mentioned above by improving flexibility and scalability. This approach is illustrated and demonstrated on a flight management system. The model is flexible enough to be adapted to different architectural solutions with a minimal effort by changing its underlying Model of Computation and Communication (MoCC). Being completely platform independent, from the same model it is possible to explore various solutions on different execution platforms.This work has been partially funded by the EU and the Spanish MICINN through the ECSEL MegaMart and Comp4Drones projects and the TEC2017-86722-C4-3-R PLATINO project

The Value Proposition of Service-Oriented Architecture

The author of this thesis evaluates Service-Oriented Architecture (SOA) design and implementation strategies. The purpose is to provide the reader with the definition of Service-Oriented Architecture. This report discusses: (1) The definition of Service-Oriented Architecture, (2) The problems solved by Service-Oriented Architecture, (3) Application of design principles to achieve Service-Oriented Architecture. As a result of this investigation, Service-Oriented Architecture is a design style that is fundamentally about sharing and reuse of functionality across diverse applications, so that organizations can quickly adapt to changing business requirements while increasing IT asset reuse and minimizing integration and development costs

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

Business rules based legacy system evolution towards service-oriented architecture.

Enterprises can be empowered to live up to the potential of becoming dynamic, agile and real-time. Service orientation is emerging from the amalgamation of a number of key business, technology and cultural developments. Three essential trends in particular are coming together to create a new revolutionary breed of enterprise, the service-oriented enterprise (SOE): (1) the continuous performance management of the enterprise; (2) the emergence of business process management; and (3) advances in the standards-based service-oriented infrastructures. This thesis focuses on this emerging three-layered architecture that builds on a service-oriented architecture framework, with a process layer that brings technology and business together, and a corporate performance layer that continually monitors and improves the performance indicators of global enterprises provides a novel framework for the business context in which to apply the important technical idea of service orientation and moves it from being an interesting tool for engineers to a vehicle for business managers to fundamentally improve their businesses

Clustering and its Application in Requirements Engineering

Large scale software systems challenge almost every activity in the software development life-cycle, including tasks related to eliciting, analyzing, and specifying requirements. Fortunately many of these complexities can be addressed through clustering the requirements in order to create abstractions that are meaningful to human stakeholders. For example, the requirements elicitation process can be supported through dynamically clustering incoming stakeholders’ requests into themes. Cross-cutting concerns, which have a significant impact on the architectural design, can be identified through the use of fuzzy clustering techniques and metrics designed to detect when a theme cross-cuts the dominant decomposition of the system. Finally, traceability techniques, required in critical software projects by many regulatory bodies, can be automated and enhanced by the use of cluster-based information retrieval methods. Unfortunately, despite a significant body of work describing document clustering techniques, there is almost no prior work which directly addresses the challenges, constraints, and nuances of requirements clustering. As a result, the effectiveness of software engineering tools and processes that depend on requirements clustering is severely limited. This report directly addresses the problem of clustering requirements through surveying standard clustering techniques and discussing their application to the requirements clustering process

Share and reuse of context metadata resulting from interactions between users and heterogeneous web-based learning environments

L'intérêt pour l'observation, l'instrumentation et l'évaluation des systèmes éducatifs en ligne est devenu de plus en plus important ces dernières années au sein de la communauté des Environnements Informatique pour l'Apprentissage Humain (EIAH). La conception et le développement d'environnements d'apprentissage en ligne adaptatifs (AdWLE - Adaptive Web-based Learning Environments) représentent une préoccupation majeure aujourd'hui, et visent divers objectifs tels que l'aide au processus de réingénierie, la compréhension du comportement des utilisateurs, ou le soutient à la création de systèmes tutoriels intelligents. Ces systèmes gèrent leur processus d'adaptation sur la base d'informations détaillées reflétant le contexte dans lequel les étudiants évoluent pendant l'apprentissage : les ressour-ces consultées, les clics de souris, les messages postés dans les logiciels de messagerie instantanée ou les forums de discussion, les réponses aux questionnaires, etc. Les travaux présentés dans ce document sont destinés à surmonter certaines lacunes des systèmes actuels en fournissant un cadre dédié à la collecte, au partage et à la réutilisation du contexte représenté selon deux niveaux d'abstraction : le contexte brut (résultant des interactions directes entre utilisateurs et applications) et le contexte inféré (calculé à partir des données du contexte brut). Ce cadre de travail qui respecte la vie privée des usagers est fondé sur un standard ouvert dédié à la gestion des systèmes, réseaux et applications. Le contexte spécifique aux outils hétérogènes constituant les EIAHs est représenté par une structure unifiée et extensible, et stocké dans un référentiel central. Pour faciliter l'accès à ce référentiel, nous avons introduit une couche intermédiaire composée d'un ensemble d'outils. Certains d'entre eux permettent aux utilisateurs et applications de définir, collecter, partager et rechercher les données de contexte qui les intéressent, tandis que d'autres sont dédiés à la conception, au calcul et à la délivrance des données de contexte inférées. Pour valider notre approche, une mise en œuvre du cadre de travail proposé intègre des données contextuelles issues de trois systèmes différents : deux plates-formes d'apprentissage Moodle (celle de l'Université Paul Sabatier de Toulouse, et une autre déployée dans le cadre du projet CONTINT financé par l'Agence Nationale de la Recherche) et une instanciation locale du moteur de recherche de la fondation Ariadne. A partir des contextes collectés, des indicateurs pertinents ont été calculés pour chacun de ces environnements. En outre, deux applications qui exploitent cet ensemble de données ont été développées : un système de recommandation personnalisé d'objets pédagogiques ainsi qu'une application de visualisation fondée sur les technologies tactiles pour faciliter la navigation au sein de ces données de contexte.An interest for the observation, instrumentation, and evaluation of online educational systems has become more and more important within the Technology Enhanced Learning community in the last few years. Conception and development of Adaptive Web-based Learning Environments (AdWLE) in order to facilitate the process of re-engineering, to help understand users' behavior, or to support the creation of Intelligent Tutoring Systems represent a major concern today. These systems handle their adaptation process on the basis of detailed information reflecting the context in which students evolve while learning: consulted resources, mouse clicks, chat messages, forum discussions, visited URLs, quizzes selections, and so on. The works presented in this document are intended to overcome some issues of the actual systems by providing a privacy-enabled framework dedicated to the collect, share and reuse of context represented at two abstraction levels: raw context (resulting from direct interactions between users and applications) and inferred context (calculated on the basis of raw context). The framework is based on an open standard dedicated to system, network and application management, where the context specific to heterogeneous tools is represented as a unified and extensible structure and stored into a central repository. To facilitate access to this context repository, we introduced a middleware layer composed of a set of tools. Some of them allow users and applications to define, collect, share and search for the context data they are interested in, while others are dedicated to the design, calculation and delivery of inferred context. To validate our approach, an implementation of the suggested framework manages context data provided by three systems: two Moodle servers (one running at the Paul Sabatier University of Toulouse, and the other one hosting the CONTINT project funded by the French National Research Agency) and a local instantiation of the Ariadne Finder. Based on the collected context, relevant indicators have been calculated for each one of these environments. Furthermore, two applications which reuse the encapsulated context have been developed on top of the framework: a personalized system for recommending learning objects to students, and a visualization application which uses multi-touch technologies to facilitate the navigation among collected context entities