14 research outputs found

    A Framework for Virtual Training in Crisis Context and a Focus on the Animation Component: The Gamemaster Workshop

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    Critical infrastructures, historical places, and sensitive buildings are vulnerable to crises, from natural to man-made disasters. Public institutions and practitioners have to train, before the occurrence of a major event, to be prepared to respond to the crisis. These sites are difficult to vacate just to perform exercises. To propose training adapted to the needs the trainer have to create an exercise dedicated to the aimed skills. Stakeholders responding to high-risk situations are asking for new ways to train and compensate for the weaknesses of existing training approaches. The research work presented in this paper concerns the design of immersive environments that can be used to create, implement and simulate crisis scenarios to improve collaborative training

    A Conceptual Framework and a Suite of Tools to Support Crisis Management

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    This article aims at describing an approach to support crisis management. The main idea is to use an original vision of Big-Data to manage the question of collaboration issues in crisis response. On the one hand, this article introduces a general framework that structures the methodology applied in our approach. This framework includes several technical and business dimensions and embeds scientific results that are presented in this article or have been described in previous articles. On the other hand, the resulting implemented suite of tools is also presented with regards to the conceptual framework. Finally, in order to emphasize all the main features described in this article, both the framework and the suite of tools are illustrated and put into action through a scenario extracted from a real exercise

    A new emergency decision support system: the automatic interpretation and contextualisation of events to model a crisis situation in real-time

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    This paper studies, designs and implements a new type of emergency decision support system that aims to improve the decision-making of emergency managers in crisis situations by connecting them to new, multiple data sources. The system combines event-driven and model-driven architectures and is dedicated to crisis cells. After its implementation, the system is evaluated using a realistic crisis scenario, in terms of its user interfaces, its ability to interpret data in real time and its ability to manage the 4Vs of Big Data. The input events correspond to traffic measurements, water levels, water flows, water predictions and flow predictions made available by French official services. The main contributions of this study are: (i) the connection between a complex event processing engine and a graph database containing the model of the crisis situation and (ii) the continuous updating of a common operational picture for the benefit of emergency managers. This study could be used as a framework for future research works on decision support systems facing complex, evolving situations

    Real-time data exploitation supported by model- and event-driven architecture to enhance situation awareness, application to crisis management

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    An effective crisis response requires up-to-date information. The crisis cell must reach for new, external, data sources. However, new data lead to new issues: their volume, veracity, variety or velocity cannot be managed by humans only, especially under high stress and time pressure. This paper proposes (i) a framework to enhance situation awareness while managing the 5Vs of Big Data, (ii) general principles to be followed and (iii) a new architecture implementing the proposed framework. The latter merges event-driven and model-driven architectures. It has been tested on a realistic flood scenario set up by official French services

    An AI framework and a metamodel for collaborative situations: Application to crisis management contexts

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    Identifying, designing, deploying and maintaining accurate collaborative networks of organizations (e.g. responders in a crisis situation) are key activities in nowadays ecosystems. However, there is a lack regarding formal approaches dedicated to characterize collaborative networks of organizations. Formal descriptions of collaborative situations, that could be used, transformed, computed and exploited would be of great benefit for the quality of such collaborative networks. This article presents a model‐based AI framework for describing collaborative situations and the associated formal metamodel dedicated to be instantiated to characterize collaborative situations in a very wide range of application domains. This metamodel (describing collaborative situation between organizations) is structured according to four complementary dimensions: the context (social, physical and geographical environment), the partners (the involved organizations, their capabilities resources and relations), the objectives (the aims of the network, the goals to be the achieved and the risks to avoid, etc.) and the behaviour (the collaborative processes to be implemented by the partners to achieve the objectives in the considered context). Besides, this metamodel can be extended for some precise application domains. This article focuses on this mechanism in the specific context of crisis management

    Interprétation automatique de données hétérogÚnes pour la modélisation de situations collaboratives : application à la gestion de crise

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    The present work is applied to the field of French crisis management, and specifically to the crisis response phase which follows a major event, like a flood or an industrial accident. In the aftermath of the event, crisis cells are activated to prevent and deal with the consequences of the crisis. They face, in a hurry, many difficulties. The stakeholders are numerous, autonomous and heterogeneous, the coexistence of contingency plans favours contradictions and the interconnections of networks promotes cascading effects. These observations arise as the volume of data available continues to grow. They come, for example, from sensors, social media or volunteers on the crisis theatre. It is an occasion to design an information system able to collect the available data to interpret them and obtain information suited to the crisis cells. To succeed, it will have to manage the 4Vs of Big Data: the Volume, the Variety and Veracity of data and information, while following the dynamic (velocity) of the current crisis. Our literature review on the different parts of this architecture enables us to define such an information system able to (i) receive different types of events emitted from data sources both known and unknown, (ii) to use interpretation rules directly deduced from official business rules and (iii) to structure the information that will be used by the stake-holders. Its architecture is event-driven and coexists with the service oriented architecture of the software developed by the CGI laboratory. The implemented system has been tested on the scenario of a 1/100 per year flood elaborated by two French forecasting centres. The model describing the current crisis situation, deduced by the proposed information system, can be used to (i) deduce a crisis response process, (ii) to detect unexpected situations, and (iii) to update a COP suited to the decision-makers.Les travaux prĂ©sentĂ©s dans ce manuscrit s’appliquent au domaine de la gestion de crise française, et notamment Ă  la phase de rĂ©ponse qui suit un Ă©vĂšnement majeur, comme une crue ou un accident industriel. Suite Ă  l’évĂšnement, des cellules de crise sont activĂ©es pour prĂ©venir et traiter les consĂ©quences de la crise. Elles font face, dans l’urgence, Ă  de nombreuses difficultĂ©s. Les parties-prenantes sont nombreuses, autonomes et hĂ©tĂ©rogĂšnes, la coexistence de plans d’urgence engendre des contradictions et des effets en cascade se nourrissent des interconnexions entre rĂ©seaux. Ces constats arrivent alors que les donnĂ©es disponibles sur les rĂ©seaux informatiques ne cessent de se multiplier. Elles sont, par exemple, Ă©mises par des capteurs de mesures, sur des rĂ©seaux sociaux, ou par des bĂ©nĂ©voles. Ces donnĂ©es sont l’occasion de concevoir un systĂšme d’information capable de les collecter pour les interprĂ©ter en un ensemble d’information formalisĂ©, utilisable en cellule de crise. Pour rĂ©ussir, les dĂ©fis liĂ©s aux 4Vs du Big data doivent ĂȘtre relevĂ©s en limitant le Volume, unifiant (la VariĂ©tĂ©) et amĂ©liorant la VĂ©racitĂ© des donnĂ©es et des informations manipulĂ©es, tout en suivant la dynamique (VĂ©locitĂ©) de la crise en cours. Nos Ă©tats de l’art sur les diffĂ©rentes parties de l’architecture recherchĂ©e nous ont permis de dĂ©finir un tel systĂšme d’information. Ce dernier est aujourd’hui capable de (i) recevoir plusieurs types d’évĂšnements Ă©mis de sources de donnĂ©es connues ou inconnues, (ii) d’utiliser des rĂšgles d’interprĂ©tations directement dĂ©duites de rĂšgles mĂ©tiers rĂ©elles et (iii) de formaliser l’ensemble des informations utiles aux parties-prenantes. Son architecture fait partie des architectures orientĂ©es Ă©vĂšnements, et coexiste avec l’architecture orientĂ©e services du logiciel dĂ©veloppĂ© par le laboratoire Centre de GĂ©nie Industriel (CGI). Le systĂšme d’information ainsi implĂ©mentĂ© a pu ĂȘtre Ă©prouvĂ© sur un scĂ©nario de crue majeure en Loire Moyenne, Ă©laborĂ© par deux Services de PrĂ©vision des Crues (SPC) français. Le modĂšle dĂ©crivant la situation de crise courante, obtenu par le systĂšme d’information proposĂ©, peut ĂȘtre utilisĂ© pour (i) dĂ©duire un processus de rĂ©ponse Ă  la crise, (ii) dĂ©tecter des imprĂ©vus ou (iii) mettre Ă  jour une reprĂ©sentation de la situation en cellule de crise

    Automatic interpretation of heterogeneous data to model collaborative situations : application to crisis management

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    Les travaux prĂ©sentĂ©s dans ce manuscrit s’appliquent au domaine de la gestion de crise française, et notamment Ă  la phase de rĂ©ponse qui suit un Ă©vĂšnement majeur, comme une crue ou un accident industriel. Suite Ă  l’évĂšnement, des cellules de crise sont activĂ©es pour prĂ©venir et traiter les consĂ©quences de la crise. Elles font face, dans l’urgence, Ă  de nombreuses difficultĂ©s. Les parties-prenantes sont nombreuses, autonomes et hĂ©tĂ©rogĂšnes, la coexistence de plans d’urgence engendre des contradictions et des effets en cascade se nourrissent des interconnexions entre rĂ©seaux. Ces constats arrivent alors que les donnĂ©es disponibles sur les rĂ©seaux informatiques ne cessent de se multiplier. Elles sont, par exemple, Ă©mises par des capteurs de mesures, sur des rĂ©seaux sociaux, ou par des bĂ©nĂ©voles. Ces donnĂ©es sont l’occasion de concevoir un systĂšme d’information capable de les collecter pour les interprĂ©ter en un ensemble d’information formalisĂ©, utilisable en cellule de crise. Pour rĂ©ussir, les dĂ©fis liĂ©s aux 4Vs du Big data doivent ĂȘtre relevĂ©s en limitant le Volume, unifiant (la VariĂ©tĂ©) et amĂ©liorant la VĂ©racitĂ© des donnĂ©es et des informations manipulĂ©es, tout en suivant la dynamique (VĂ©locitĂ©) de la crise en cours. Nos Ă©tats de l’art sur les diffĂ©rentes parties de l’architecture recherchĂ©e nous ont permis de dĂ©finir un tel systĂšme d’information. Ce dernier est aujourd’hui capable de (i) recevoir plusieurs types d’évĂšnements Ă©mis de sources de donnĂ©es connues ou inconnues, (ii) d’utiliser des rĂšgles d’interprĂ©tations directement dĂ©duites de rĂšgles mĂ©tiers rĂ©elles et (iii) de formaliser l’ensemble des informations utiles aux parties-prenantes. Son architecture fait partie des architectures orientĂ©es Ă©vĂšnements, et coexiste avec l’architecture orientĂ©e services du logiciel dĂ©veloppĂ© par le laboratoire Centre de GĂ©nie Industriel (CGI). Le systĂšme d’information ainsi implĂ©mentĂ© a pu ĂȘtre Ă©prouvĂ© sur un scĂ©nario de crue majeure en Loire Moyenne, Ă©laborĂ© par deux Services de PrĂ©vision des Crues (SPC) français. Le modĂšle dĂ©crivant la situation de crise courante, obtenu par le systĂšme d’information proposĂ©, peut ĂȘtre utilisĂ© pour (i) dĂ©duire un processus de rĂ©ponse Ă  la crise, (ii) dĂ©tecter des imprĂ©vus ou (iii) mettre Ă  jour une reprĂ©sentation de la situation en cellule de crise.The present work is applied to the field of French crisis management, and specifically to the crisis response phase which follows a major event, like a flood or an industrial accident. In the aftermath of the event, crisis cells are activated to prevent and deal with the consequences of the crisis. They face, in a hurry, many difficulties. The stakeholders are numerous, autonomous and heterogeneous, the coexistence of contingency plans favours contradictions and the interconnections of networks promotes cascading effects. These observations arise as the volume of data available continues to grow. They come, for example, from sensors, social media or volunteers on the crisis theatre. It is an occasion to design an information system able to collect the available data to interpret them and obtain information suited to the crisis cells. To succeed, it will have to manage the 4Vs of Big Data: the Volume, the Variety and Veracity of data and information, while following the dynamic (velocity) of the current crisis. Our literature review on the different parts of this architecture enables us to define such an information system able to (i) receive different types of events emitted from data sources both known and unknown, (ii) to use interpretation rules directly deduced from official business rules and (iii) to structure the information that will be used by the stake-holders. Its architecture is event-driven and coexists with the service oriented architecture of the software developed by the CGI laboratory. The implemented system has been tested on the scenario of a 1/100 per year flood elaborated by two French forecasting centres. The model describing the current crisis situation, deduced by the proposed information system, can be used to (i) deduce a crisis response process, (ii) to detect unexpected situations, and (iii) to update a COP suited to the decision-makers

    The Periodic Table of Industries: Detection of Collaboration Opportunities Based on an Imitation of the Mendeleev Periodic Table of Elements

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    The concept of collaborative networks has been encountered very often lately as the answer when trying to adapt and improve enterprises in these highly competitive business environments, therefore the urge for constantly addressing this topic. A lot of work-related to collaborative networks has been done so far, from defining network types to leveling partnerships and proposing models for partnership developments. But the lack of tackling a very important obstacle, which is the difficulty of detecting and anticipating collaboration opportunities between enterprises, inspired this research. In this article, a new theoretical opportunity detection approach is proposed based on enterprise characterization concept, KPI classification as well as collaboration types. This detection approach is a table of industrial classifications that imitates the Mendeleev periodic table from the concept point of view. A fictional example from an industrial context is shown to explain the usage of this approach accompanied by discussion about future work and limitations

    Génération d'un modÚle de situation en temps réel : application à la gestion de crise

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    Le congrĂšs a pour titre "L'essor des systĂšmes connectĂ©s dans l'industrie et les services"International audienceChaque jour, de plus en plus de sources de donnĂ©es sont rendues accessibles, et ce Ă  l’échelle mondiale. Ceci constitue une opportunitĂ© sans pareil pour les organisations d’amĂ©liorer leur connaissance de la situation («situational awareness») dans un environnement de plus en plus instable et imprĂ©visible. Cette connaissance sert de base Ă  la prise de meilleures dĂ©cisions et ce dans un laps de temps plus court qu’auparavant. Le projet GĂ©NĂ©Pi a vu le jour afin d’aider cette prise de dĂ©cision, dans un contexte multi-acteurs. Il vise Ă  faciliter la collaboration inter-organisationnelle, en temps rĂ©el, quelque soit la complexitĂ© ou l’instabilitĂ© de l’environnement collaboratif. Afin de dĂ©finir et tester la solution proposĂ©e par GĂ©NĂ©Pi, un cas d’étude de gestion de crise a Ă©tĂ© dĂ©veloppĂ© en partenariat avec des institutionnels. Cet article prĂ©sente une nouvelle approche pour automatiser la crĂ©ation et la mise Ă  jour d’un modĂšle reprĂ©sentatif de la situation, en temps rĂ©el, afin de : (i) fournir aux dĂ©cideurs une vue opĂ©rationnelle commune, (ii) Ă©tablir un cadre de dĂ©finition et de maintien des processus collaboratifs au cours du temps afin de veiller Ă  la bonne poursuite des objectifs du rĂ©seau collaboratif
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