8,933 research outputs found

    Visualization of Academic Quality Assurance Metamodel Through the Creation of Academic Quality Assurance Metamodel Information System

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    Academic quality assurance metamodel information system (AQAMIS) is a mobile-web-friendly system designed to manage academic quality assurance (AQA) knowledge structure for higher education using the AQA metamodel structure. This research describes the development and functionality of AQAMIS, as well as how it visualizes the AQA metamodel on a system-based level. The AQAMIS system transformed the metamodel class diagram design into a user-friendly design, making it easier for any non-technical user to understand the metamodel design. The AQAMIS is composed of two major parts: the AQA metamodel and the knowledge repository system. The metamodel addresses the issue of managing knowledge for quality assurance in higher education. While the system resolves the issue of sharing best practices in higher education AQA. The AQAMIS system assists in ensuring that academic quality assurance systems are implemented more efficiently and effectively in higher learning institutions (HLIs). AQAMIS is also a one-stop center for respective users such as HLI top management, policymakers, auditors, and quality assurance personnel to access their expertise and share best practices in AQA endeavors

    Disaster Management (DM) Model Transformations Framework

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    Metamodelling produces a ‘metamodel’ capable of generalizing the domain. A metamodel gathers all domain concepts and their relationships. It enables partitioning a domain problem into sub-problems. Decision makers can then develop a variety of domain solutions models based on mixing and matching solutions for sub-problems indentified using the metamodel. A repository of domain knowledge structured using the metamodel would allow the transformation of models generated from a higher level to a lower level according to scope of the problem on hand. In this paper, we reveal how a process of mixing and matching disaster management actions can be accomplished using our Disaster Management Metamodel (DMM). The paper describes DM model transformations underpinned by DMM. They are illustrated benefiting DM users creating appropriate DM solution models from extant partial solutions

    DM model transformations framework

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    Metamodelling produces a \u27metamodel\u27 capable of generalizing the domain. A metamodel gathers all domain concepts and their relationships. It enables partitioning a domain problem into sub-problems. Decision makers can then develop a variety of domain solutions models based on mixing and matching solutions for sub-problems indentified using the metamodel. A repository of domain knowledge structured using the metamodel would allow the transformation of models generated from a higher level to a lower level according to scope of the problem on hand. In this paper, we reveal how a process of mixing and matching disaster management actions can be accomplished using our Disaster Management Metamodel (DMM). The paper describes DM model transformations underpinned by DMM. They are illustrated benefiting DM users creating appropriate DM solution models from extant partial solutions

    A Dashboard to Support Decision-Making Processes in Learning Ecosystems

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    There are software solutions to solve most of the problems related to information management in any company or institutions, but still, there is a problem for transforming information into knowledge. Technological ecosystems emerge as a solution to combine existing tools and human resources to solve different problems of knowledge management. In particular, when the ecosystem is focused on learning processes associated with knowledge are named learning ecosystems. The learning ecosystem metamodel defined in previous works solves several problems related to the definition and implementation of these solutions. However, there are still challenges associated with improving the analysis and visualization of information as a way to discover knowledge and support decision making processes. On the other hand, there is a metamodel proposal to define customized dashboards for supporting decision-making processes. This proposal aims to integrate both metamodels as a way to improve the definition of learning ecosystems

    Metamodel Instance Generation: A systematic literature review

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    Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page

    A meta-model to develop learning ecosystems with support for knowledge discovery and decisionmaking processes.

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    There are software solutions to solve most of the problems related to information management in any company or institutions, but still, there is a problem for transforming information into knowledge. Technological ecosystems emerge as a solution to combine existing tools and human resources to solve different problems of knowledge management. In particular, when the ecosystem is focused on learning processes associated with knowledge are named learning ecosystems. The learning ecosystem metamodel defined in previous works solves several problems related to the definition and implementation of these solutions. However, there are still challenges associated with improving the analysis and visualization of information as a way to discover knowledge and support decision making processes. On the other hand, there is a metamodel proposal to define customized dashboards for supporting decision-making processes. This proposal aims to integrate both metamodels as a way to improve the definition of learning ecosystems

    Knowledge Reuse for Customization: Metamodels in an Open Design Community for 3d Printing

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    Theories of knowledge reuse posit two distinct processes: reuse for replication and reuse for innovation. We identify another distinct process, reuse for customization. Reuse for customization is a process in which designers manipulate the parameters of metamodels to produce models that fulfill their personal needs. We test hypotheses about reuse for customization in Thingiverse, a community of designers that shares files for three-dimensional printing. 3D metamodels are reused more often than the 3D models they generate. The reuse of metamodels is amplified when the metamodels are created by designers with greater community experience. Metamodels make the community's design knowledge available for reuse for customization-or further extension of the metamodels, a kind of reuse for innovation

    Validation of the learning ecosystem metamodel using transformation rules

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    The learning ecosystem metamodel is a platform-independent model to define learning ecosystems. It is based on the architectural pattern for learning ecosystems. To ensure the quality of the learning ecosystem metamodel is necessary to validate it through a Model-to-Model transformation. Specifically, it is required to verify that the learning ecosystem metamodel allows defining real learning ecosystems based on the architectural pattern. Although this transformation can be done manually, the use of tools to automate the process ensures its validity and minimize the risk of bias. This work describes the validations process composed of eight phases and the results obtained, in particular: the transformation of the MOF metamodel to Ecore to use stable tools for the validation, the definition of a platform-specific metamodel for defining learning ecosystems and the transformation from instances of the learning ecosystem metamodel to instances of the platform-specific metamodel using ATL. A quality framework has been applied to the three metamodels involved in the process to guarantee the quality of the results. Furthermore, some phases have been used to review and improve the learning ecosystem metamodel in Ecore. Finally, the result of the process demonstrates that the learning ecosystem metamodel is valid. Namely, it allows defining models that represent learning ecosystems based on the architectural pattern that can be deployed in real contexts to solve learning and knowledge management problem

    Enterprise architecture for small and medium-sized enterprises

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    Enterprise architecture (EA) is used as a holistic approach to keep things aligned in a company. Some emphasize the use of EA to align IT with the business, others see it broader and use it to also keep the processes aligned with the strategy. Although a lot of research is being done on EA, still hardly anything is known about its use in the context of a small and medium sized enterprise (SME). Because of some specific characteristics of SMEs, it is interesting to look how EA can be applied in a SME. In this PhD, we present an approach for EA for SMEs, which combines four dimensions to get a holistic overview, while keeping things aligned. The approach is developed with special attention towards the characteristics of SMEs. Case studies are used to refine the metamodel and develop an adequate method, while tool support is being developed to enable the validation rounds
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