343 research outputs found

    Supporting Salespersons CRM Efforts through Location Based Mobile Support Systems

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    The paper aims at assessing how can location based mobile support systems support salespersonsÂŽ CRM efforts when they are operating within a highly mobile work environment. The paper is structured as follows. In the first section the paper conceptualises key properties of location based mobile support systems. It then introduces the dual role of salespeople in CRM through a brief literature review. A fourth section suggests potential mobile location services and applications that can support salespersons in performing effectively their everyday CRM tasks and links such applications to the determinant of salespersonsÂŽ performance. The papers concludes with some remarks and suggests some areas of further researc

    A group learning management method for intelligent tutoring systems

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    In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture

    A Framework for an adaptive grid scheduling: an organizational perspective

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    Grid systems are complex computational organizations made of several interacting components evolving in an unpredictable and dynamic environment. In such context, scheduling is a key component and should be adaptive to face the numerous disturbances of the grid while guaranteeing its robustness and efficiency. In this context, much work remains at low-level focusing on the scheduling component taken individually. However, thinking the scheduling adaptiveness at a macro level with an organizational view, through its interactions with the other components, is also important. Following this view, in this paper we model a grid system as an agent-based organization and scheduling as a cooperative activity. Indeed, agent technology provides high level organizational concepts (groups, roles, commitments, interaction protocols) to structure, coordinate and ease the adaptation of distributed systems efficiently. More precisely, we make the following contributions. We provide a grid conceptual model that identifies the concepts and entities involved in the cooperative scheduling activity. This model is then used to define a typology of adaptation including perturbing events and actions to undertake in order to adapt. Then, we provide an organizational model, based on the Agent Group Role (AGR) meta-model of Freber, to support an adaptive scheduling at the organizational level. Finally, a simulator and an experimental evaluation have been realized to demonstrate the feasibility of our approach

    Pattern-based refactoring in model-driven engineering

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    L’ingĂ©nierie dirigĂ©e par les modĂšles (IDM) est un paradigme du gĂ©nie logiciel qui utilise les modĂšles comme concepts de premier ordre Ă  partir desquels la validation, le code, les tests et la documentation sont dĂ©rivĂ©s. Ce paradigme met en jeu divers artefacts tels que les modĂšles, les mĂ©ta-modĂšles ou les programmes de transformation des modĂšles. Dans un contexte industriel, ces artefacts sont de plus en plus complexes. En particulier, leur maintenance demande beaucoup de temps et de ressources. Afin de rĂ©duire la complexitĂ© des artefacts et le coĂ»t de leur maintenance, de nombreux chercheurs se sont intĂ©ressĂ©s au refactoring de ces artefacts pour amĂ©liorer leur qualitĂ©. Dans cette thĂšse, nous proposons d’étudier le refactoring dans l’IDM dans sa globalitĂ©, par son application Ă  ces diffĂ©rents artefacts. Dans un premier temps, nous utilisons des patrons de conception spĂ©cifiques, comme une connaissance a priori, appliquĂ©s aux transformations de modĂšles comme un vĂ©hicule pour le refactoring. Nous procĂ©dons d’abord par une phase de dĂ©tection des patrons de conception avec diffĂ©rentes formes et diffĂ©rents niveaux de complĂ©tude. Les occurrences dĂ©tectĂ©es forment ainsi des opportunitĂ©s de refactoring qui seront exploitĂ©es pour aboutir Ă  des formes plus souhaitables et/ou plus complĂštes de ces patrons de conceptions. Dans le cas d’absence de connaissance a priori, comme les patrons de conception, nous proposons une approche basĂ©e sur la programmation gĂ©nĂ©tique, pour apprendre des rĂšgles de transformations, capables de dĂ©tecter des opportunitĂ©s de refactoring et de les corriger. Comme alternative Ă  la connaissance disponible a priori, l’approche utilise des exemples de paires d’artefacts d’avant et d’aprĂšs le refactoring, pour ainsi apprendre les rĂšgles de refactoring. Nous illustrons cette approche sur le refactoring de modĂšles.Model-Driven Engineering (MDE) is a software engineering paradigm that uses models as first-class concepts from which validation, code, testing, and documentation are derived. This paradigm involves various artifacts such as models, meta-models, or model transformation programs. In an industrial context, these artifacts are increasingly complex. In particular, their maintenance is time and resources consuming. In order to reduce the complexity of artifacts and the cost of their maintenance, many researchers have been interested in refactoring these artifacts to improve their quality. In this thesis, we propose to study refactoring in MDE holistically, by its application to these different artifacts. First, we use specific design patterns, as an example of prior knowledge, applied to model transformations to enable refactoring. We first proceed with a detecting phase of design patterns, with different forms and levels of completeness. The detected occurrences thus form refactoring opportunities that will be exploited to implement more desirable and/or more complete forms of these design patterns. In the absence of prior knowledge, such as design patterns, we propose an approach based on genetic programming, to learn transformation rules, capable of detecting refactoring opportunities and correcting them. As an alternative to prior knowledge, our approach uses examples of pairs of artifacts before and after refactoring, in order to learn refactoring rules. We illustrate this approach on model refactoring

    Introduction to Collaborative Technology for Coordinating Crisis Management (CT2CM) track

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    International audienceThis is the foreword introduction to the special Wetice Track about Coordination in Crisis Management and its support technology

    Flexible and Emergent Workflows using Adaptive Agents

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    International audienceMost of existing workflow systems are rigid since they require to completely specify processes before their enactment and they also lack flexibility during their execution. This work proposes to view a workflow as a set of cooperative and adaptive agents interleaving its design and its execution leading to an emergent workflow. We use the theory of Adaptive Multi-Agent Systems (AMAS) to provide agents with adaptive capabilities and the whole multi-agent system with emergent "feature". We provide a meta-model linking workflow and AMAS concepts, and the specification of agent behavior and the resulting collaborations. A simulator has been implemented with the Make Agent Yourself platform
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