48,047 research outputs found

    Modelling Goal Dependencies and Domain Model Together

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    AbstractSeveral actors such as human, organization, software applications and hardware units perform our daily activities such as medical care, entertainment and so on. We call each daily activity a socio-technical system (STS), and we also call actors except human and organizations Machines. Human and organizations in an STS become better than ever when new Machines are introduced into the STS and they are beneficial to human and organizations. Although modelling goal dependencies in such a STS contributes to identifying beneficial Machines because such a dependency can represent an actor asks some Machine to achieve his own goal. It is however not easy for modelers to describe a correct dependency. We thus proposed and exemplified an extended modelling notation called Goal Dependency Model with Objects (GDMO) based on strategic dependency (SD) in i*. In GDMO, objects related to a goal in an SD are explicitly specified. Modelers can determine an actor has the right to want the goal to be achieved because relationships between the actor and the objects such as ownership clarify the right. They can also determine another actor has the ability to achieve the goal. In addition, relationships among objects, i.e. a domain model, can suggest missing SDs, and the boundary of an STS can be determined without omission

    CML: the commonKADS conceptual modelling language

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    We present a structured language for the specification of knowledge models according to the CommonKADS methodology. This language is called CML (Conceptual Modelling Language) and provides both a structured textual notation and a diagrammatic notation for expertise models. The use of our CML is illustrated by a variety of examples taken from the VT elevator design system

    The i* framework for goal-oriented modeling

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    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-39417-6i* is a widespread framework in the software engineering field that supports goal-oriented modeling of socio-technical systems and organizations. At its heart lies a language offering concepts such as actor, dependency, goal and decomposition. i* models resemble a network of interconnected, autonomous, collaborative and dependable strategic actors. Around this language, several analysis techniques have emerged, e.g. goal satisfaction analysis and metrics computation. In this work, we present a consolidated version of the i* language based on the most adopted versions of the language. We define the main constructs of the language and we articulate them in the form of a metamodel. Then, we implement this version and a concrete technique, goal satisfaction analys is based on goal propagation, using ADOxx. Throughout the chapter, we used an example based on open source software adoption to illustrate the concepts and test the implementation.Peer ReviewedPostprint (author's final draft

    Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks

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    In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.). Recurrent Neural Networks (RNNs) have proven to be very successful for modelling sequences of data in many areas of Machine Learning. In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events. We work with a database collected in the Charit\'{e} Hospital in Berlin that contains complete information concerning patients that underwent a kidney transplantation. After the transplantation three main endpoints can occur: rejection of the kidney, loss of the kidney and death of the patient. Our goal is to predict, based on information recorded in the Electronic Health Record of each patient, whether any of those endpoints will occur within the next six or twelve months after each visit to the clinic. We compared different types of RNNs that we developed for this work, with a model based on a Feedforward Neural Network and a Logistic Regression model. We found that the RNN that we developed based on Gated Recurrent Units provides the best performance for this task. We also used the same models for a second task, i.e., next event prediction, and found that here the model based on a Feedforward Neural Network outperformed the other models. Our hypothesis is that long-term dependencies are not as relevant in this task

    Designinig Coordination among Human and Software Agents

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    The goal of this paper is to propose a new methodology for designing coordination between human angents and software agents and, ultimately, among software agents. The methodology is based on two key ideas. The first is that coordination should be designed in steps, according to a precise software engineering methodology, and starting from the specification of early requirements. The second is that coordination should be modeled as dependency between actors. Two actors may depend on one another because they want to achieve goals, acquire resources or execute a plan. The methodology used is based on Tropos, an agent oriented software engineering methodology presented in earlier papers. The methodology is presented with the help of a case study

    StarGro: Building i* metrics for agile methodologies

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    Requirements management is one of the cornerstone activities in software development. Agile methodologies use dedicated methods, techniques and artifacts in order to implement this activity. Remarkably, Backlog Grooming is the activity of managing and welcoming changing requirements in SCRUM. However, current industrial practices in agile development still tend to render this process in the shape of a list of statements, features and bug fixes that often leads to a blurred view of the goals of the project, the underestimation of client's needs and the decrease of the ability to respond to changes. In this paper we outline an approach that uses goal and agent oriented modelling techniques in order to fill in this "intentional" gap that current industrial approaches lack.Peer ReviewedPostprint (published version

    On interoperability and conformance assessment in service composition

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    The process of composing a service from other services typically involves multiple models. These models may represent the service from distinct perspectives, e.g., to model the different roles of systems involved in the service, and at distinct abstraction levels, e.g., to model the service’s capability, interface or the orchestration that implements the service. The consistency among these models needs to be maintained in order to guarantee the correctness of the composition process. Two types of consistency relations are distinguished: interoperability, which concerns the ability of different roles to interoperate, and conformance, which concerns the correct implementation of an abstract model by a more concrete model. This paper discusses the need for and use of techniques to assess interoperability and conformance in a service composition process. The paper shows how these consistency relations can be described and analysed using concepts from the COSMO framework. Examples are presented to illustrate how interoperability and conformance can be assessed

    A conceptual architecture for semantic web services development and deployment

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    Several extensions of the Web Services Framework (WSF) have been proposed. The combination with Semantic Web technologies introduces a notion of semantics, which can enhance scalability through automation. Service composition to processes is an equally important issue. Ontology technology – the core of the Semantic Web – can be the central building block of an extension endeavour. We present a conceptual architecture for ontology-based Web service development and deployment. The development of service-based software systems within the WSF is gaining increasing importance. We show how ontologies can integrate models, languages, infrastructure, and activities within this architecture to support reuse and composition of semantic Web services

    Model-Based Mitigation of Availability Risks

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    The assessment and mitigation of risks related to the availability of the IT infrastructure is becoming increasingly important in modern organizations. Unfortunately, present standards for Risk Assessment and Mitigation show limitations when evaluating and mitigating availability risks. This is due to the fact that they do not fully consider the dependencies between the constituents of an IT infrastructure that are paramount in large enterprises. These dependencies make the technical problem of assessing availability issues very challenging. In this paper we define a method and a tool for carrying out a Risk Mitigation activity which allows to assess the global impact of a set of risks and to choose the best set of countermeasures to cope with them. To this end, the presence of a tool is necessary due to the high complexity of the assessment problem. Our approach can be integrated in present Risk Management methodologies (e.g. COBIT) to provide a more precise Risk Mitigation activity. We substantiate the viability of this approach by showing that most of the input required by the tool is available as part of a standard business continuity plan, and/or by performing a common tool-assisted Risk Management
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