Collaborative knowledge as a service applied to the disaster management domain

Cloud computing offers services which promise to meet continuously increasing computing demands by using a large number of networked resources. However, data heterogeneity remains a major hurdle for data interoperability and data integration. In this context, a Knowledge as a Service (KaaS) approach has been proposed with the aim of generating knowledge from heterogeneous data and making it available as a service. In this paper, a Collaborative Knowledge as a Service (CKaaS) architecture is proposed, with the objective of satisfying consumer knowledge needs by integrating disparate cloud knowledge through collaboration among distributed KaaS entities. The NIST cloud computing reference architecture is extended by adding a KaaS layer that integrates diverse sources of data stored in a cloud environment. CKaaS implementation is domain-specific; therefore, this paper presents its application to the disaster management domain. A use case demonstrates collaboration of knowledge providers and shows how CKaaS operates with simulation models

Knowledge as a Service Framework for Disaster Data Management

Each year, a number of natural disasters strike across the globe, killing hundreds and causing billions of dollars in property and infrastructure damage. Minimizing the impact of disasters is imperative in today’s society. As the capabilities of software and hardware evolve, so does the role of information and communication technology in disaster mitigation, preparation, response, and recovery. A large quantity of disaster-related data is available, including response plans, records of previous incidents, simulation data, social media data, and Web sites. However, current data management solutions offer few or no integration capabilities. Moreover, recent advances in cloud computing, big data, and NoSQL open the door for new solutions in disaster data management. In this paper, a Knowledge as a Service (KaaS) framework is proposed for disaster cloud data management (Disaster-CDM), with the objectives of 1) storing large amounts of disaster-related data from diverse sources, 2) facilitating search, and 3) supporting their interoperability and integration. Data are stored in a cloud environment using a combination of relational and NoSQL databases. The case study presented in this paper illustrates the use of Disaster-CDM on an example of simulation models

Vers les systèmes IoT autonomiques et cognitifs, application pour la gestion des traitements des patients

In this thesis, we propose a collaborative model driven methodology for designing Autonomic Cognitive IoT systems to deal with IoT design complexity. We defined within this methodology a set of autonomic cognitive design patterns that aim at (1) delineating the dynamic coordination of the autonomic processes to deal with the system's context changeability and requirements evolution at run-time, and (2) adding cognitive abilities to IoT systems to understand big data and generate new insights. To address challenges related to big data and scalability, we propose a generic semantic big data platform that aims at integrating heterogeneous distributed data sources deployed on the cloud and generating knowledge that will be exposed as a service (Knowledge as a Service--KaaS). As an application of the proposed contributions, we instantiated and combined a set of patterns for the development of prescriptive cognitive system for the patient treatment management. Thus, we elaborated two ontological models describing the wearable devices and the patient context as well as the medical knowledge for decision-making. The proposed system is evaluated from the clinical prescriptive through collaborating with medical experts, and from the performance perspective through deploying the system within the KaaS following different configurationsDans cette thèse, nous proposons une méthodologie basée sur les modèles pour gérer la complexité de la conception des systèmes autonomiques cognitifs intégrant des objets connectés. Cette méthodologie englobe un ensemble de patrons de conception dont nous avons défini pour modéliser la coordination dynamique des processus autonomiques pour gérer l’évolution des besoins du système, et pour enrichir les systèmes avec des propriétés cognitives qui permettent de comprendre les données et de générer des nouvelles connaissances. De plus, pour gérer les problèmes reliés à la gestion des big data et à la scalabilité du système lors du déploiement des processus, nous proposons une plate-forme sémantique supportant le traitement des grandes quantités de données afin d’intégrer des sources de données distribuées et hétérogènes déployées sur le cloud pour générer des connaissances qui seront exposées en tant que service (KaaS). Comme application de nos contributions, nous proposons un système cognitif prescriptif pour la gestion du plan de traitement du patient. Ainsi, nous élaborons des modèles ontologiques décrivant les capteurs et le contexte du patient, ainsi que la connaissance médicale pour la prise de décision. Le système proposé est évalué de point de vue clinique en collaborant avec des experts médicaux, et de point de vue performance en proposant des différentes configurations dans le KaaS

Two-echelons multi-depot, multi-vehicle inventory location-routing problem : GIPA's real-life application

Cette thèse présente une nouvelle problématique inspirée d'un cas réel, nous introduisons Problème de localisation-routage multi-dépôts, multi-véhicules à deux niveaux avec gestion de stock (2E-MDILRP) pour un type spécifique de produit périssable, qui s'est avéré être NP-difficile. De plus, nous avons étudié la taxonomie des sous-problèmes du 2E-MDILRP, et nous avons proposé les différentes contraintes des produits périssables. Cette taxonomie permettrait, d'une part, de mieux comprendre la 2E-MDILRP cas des produits périssables et, d'autre part, d'identifier des sujets de recherche prometteurs. Un modèle mathématique a été proposé et validé à l'aide du solveur OPL\Cplex pour cette nouvelle variante. Nous avons également proposé une heuristique spécifique améliorée avec l'algorithme de recherche locale itérative (ILS) utilisé pour résoudre le 2E-MDILRP. Cette méta-heuristique est testée dans un cadre théorique, et dans un cadre empirique réel. Ce dernier consiste à optimiser la localisation, le routage et l'inventaire des produits périssables. Ces méthodes développées ont été testées sur des jeux d'instances allant jusqu'à 4 dépôts principaux, 20 satellites potentielles et 200 clients, avec deux flottes de véhicules hétérogènes disponibles à raison d’une flotte pour chaque niveau. Les résultats de notre méthode exacte et approchées montrent l'efficacité de l'approche.This thesis presents a new problems inspired by a real case, we introduce two-echelon multi-depot, multi-vehicles Inventory-Location-Routing problem (2E-MDILRP) for a specific type of perishable product, proved to be NP-hard. In addition, we studied the taxonomy of the sub problems of 2E-MDILRP, and we propose the different constraints of perishable products. This taxonomy would, on the one hand, provide better understanding of the 2E-MDILRP of the perishable products, and on the second hand identify promising further research topics. So, a mathematical model has been proposed and validated using the OPL\Cplex solver for this new variant. Also, we proposed a specific heuristic developed with iterated local search algorithm (ILS) using to solve the new 2E-MDILRP problem. This is tested within a theoretical framework, the under a real empirical framework. The latter consists in optimizing the perishable product distribution and inventory. These developed methods have been tested on sets of instances of up to 4 main depots, 20 potential satellites and 200 customers, with two fleets of heterogeneous vehicles available at a rate of one fleet for each level. The results of our exact and approximate method show the efficiency of the approach

A Model-Driven Methodology for the Design of Autonomic and Cognitive IoT-Based Systems: Application to Healthcare

International audienceDue to its abilities to capture real-time data concerning the physical world, the Internet of Things (IoT) phenomenon is fast gaining momentum in different applicative domains. Its benefits are not limited to connecting things, but lean on how the collected data is transformed into insights and interact with domain experts for better decisions. Nonetheless, a set of challenges including the complexity of IoT-based systems and the management of the ensuing big and heterogeneous data and as well as the system scalability; need to be addressed for the development of flexible smart IoT-based systems that drive the business decision-making. Consequently, inspired from the human nervous system and cognitive abilities, we have proposed a set of autonomic cognitive design patterns that alleviate the design complexity of smart IoT-based systems, while taking into consideration big data and scalability management. The ultimate goal of these patterns is providing generic and reusable solutions for elaborating flexible smart IoT-based systems able to perceive the collected data and provide decisions. These patterns are articulated within a model-driven methodology that we have proposed to incrementally refine the system functional and nonfunctional requirements. Following the proposed methodology, we have combined and instantiated a set of patterns for developing a flexible cognitive monitoring system to manage patients' health based on heterogeneous wearable devices. We have highlighted the gained flexibility and demonstrated the ability of our system to integrate and process heterogeneous 2 large scale data streams. Finally, we have evaluated the system performance in terms of response time and scalability management

An Autonomic Cognitive Pattern for Smart IoT-based System Manageability: Application to Comorbidity Management

International audienceThe adoption of the Internet of Things (IoT) drastically witnesses an increase in different domains, and contributes to the fast digitalization of the universe. Henceforth, next generation of IoT-based systems are set to become more complex to design and manage. Collecting real-time IoT generated data unleashes a new wave of opportunities for business to take more precise and accurate decisions at the right time. However, a set of challenges including the design complexity of IoT-based systems and the management of the ensuing heterogeneous big data as well as the system scalability; need to be addressed for the development of flexible smart IoT-based systems. Consequently, we proposed a set of design patterns that diminish the system design complexity through selecting the appropriate/combination of patterns based on the system requirements. These patterns identify four maturity levels for the design and development of smart IoT-based systems. In this paper, we are mainly dealing with the system design complexity to manage the context changeability at runtime. Thus, we delineate the autonomic cognitive management pattern, which is most mature level. Based on the autonomic computing, this pattern identifies a combination of management processes able to continuously detect and manage the context changes. These processes are coordinated based on cognitive mechanisms that allow the system perceiving and understanding the meaning of the received data to take business decisions, as well as to dynamically discover new processes meeting the requirements evolution at runtime. We demonstrated the use of the proposed pattern with a use case from the healthcare domain, more precisely the patient comorbidity management based on wearables

DRAAS: Dynamically Reconfigurable Architecture for Autonomic Services

International audienceThe development and the provisioning of autonomic networked services are essential for enterprises and factories of the future. Endowing services with autonomic properties allows one to maintain at runtime the Quality of Service (QoS) including different parameters related to performance, availability and reputation such as response time and successful execution rate. Handling the autonomic properties requires the ability to deal with permanent requirement evolving and constraint changes. For instance, managing QoS degradation requires the capacity of identifying its possible or actual sources and the capacity of reconfiguration planning and execution. Dealing with these issues is especially challenging for web services since the autonomic solution has to be seamless for the service requesters, ensuring that Web Services are always usable under the different deployment constraints. To implement such autonomic systems, the literature provides different approaches, varying from the design to the full implementation of autonomic primitives. In this chapter, we present DRAAS: a Dynamically Reconfigurable Architecture for Autonomic Services able to provide autonomic properties for QoS management in web service-based distributed applications. DRAAS has been implemented and experimented successfully with different use cases. It covers the whole cycle of autonomic management including monitoring and analysis of QoS parameters , planning and execution

A Model Driven Approach for Automated Design of Context-Aware Autonomic Architectures

12 pagesIn this paper, we propose a model driven approach automating the design of autonomic systems. We handle non-functional properties with a focus on managing QoS degradation for cooperative M2M applications such as health care. Nowadays, service delivery becomes close to end-users such as M2M applications, which are being incorporated into the existing infrastructure. The Remote Health Care System and specialized sensors for in-home patient monitoring are at the current forefront of new technologies. While there are benefits from technologies such as reducing costs and medical errors, associated architecture and communication infrastructure have to ensure care continuity and quality of service (QoS). In this paper, we propose a model driven approach which enables the generation of autonomic architecture from high level functional and non-functional requirements. Our work instantiates the Model Driven Architecture (MDA) approach. We elaborate formal rules using graph grammars to transform a high level functional requirements to an autonomic architecture implemented under GMTE, a Graph Matching tool. We generate a Service Component Architecture (SCA) at the MDA low level (PSM) to implement the architecture in different technologies such as EJB, JMS, SOAP, etc. The Remote Health Care System shows the feasibility and the efficiency of our approach

A Model Driven Methodology for enabling Autonomic Reconfiguration of Service Oriented Architecture

2 pagesInternational audienceAutonomic systems are known by their abilities to manage and reconfigure themselves according to the context changes that can include the evolution of functional and/or nonfunctional requirements, without human intervention. The design and the management of such complex systems manually is a hard task since both functional and non-functional requirements should be taken into consideration. In this paper, we propose a model driven methodology which enables the dynamic reconfiguration by generating autonomic architectures from high level descriptions of functional requirements. Based on transformation and refinement rules, this methodology automates the incorporation of non-functional requirements to the initial architecture. Our work follows the Model Driven Architecture (MDA) to cover the different abstraction levels

A Three-Step Approach for Building Correct-by-Design Autonomic Service-Oriented Architectures

20Autonomic systems are known by their ability to manage and reconfigure themselves in reaction to context changes without human intervention. The manual design and management of such complex systems is an error-prone task where both functional and non-functional requirements can be disturbed. In this paper, we provide a correct-by-design approach that allows a given abstract architectural description to be refined into autonomic architecture models that are close to implementations. The challenge is to get a system architecture that includes the necessary components for monitoring the non-functional parameters (e.g. quality of service) and reacting to any degradation by performing runtime reconfigurations. For solving such a problem, we provide an automated approach where an architecture is modelled as a conceptual graph with different levels of abstractions. Nodes represent software components or services or connectors and vertices represent communication or interaction links. To endow a given architecture with such properties, we define graph transformation rules to formally refine a given abstract representation into a specific model allowing the easy implementation of the autonomic schema. Such a refined schema includes the autonomic control loop components namely Monitoring, Analysis, Planning, and Execution (MAPE). We apply our approach to the "Campus-Wide Smart Metering" use-case providing a service-oriented style connecting Machine-to-Machine devices