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20 results for “Metamodeling”
CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development
<p>Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.</p> <p>The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.</p> <p>The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. </p> <p>Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. </p> <p>Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype.</p> <p>We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.</p> <p>The table below summarizes the variables contained in the dataset, for each scenario.</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Description and unit </strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p>Scenario n°</p> </td> <td> <p>Number of the scenario.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>INPUTS (X)</strong></p> </td> </tr> <tr> <td> <p>NM</p> </td> <td> <p>Relative mean sea level, referenced to the French vertical datum (m, IGN69)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>T</p> </td> <td> <p>Tidal water level (m), referenced to the relative mean sea level</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>S</p> </td> <td> <p>Atmospheric storm surge (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Hs</p> </td> <td> <p>Significant wave height (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Tp</p> </td> <td> <p>Wave peak period (s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Dp</p> </td> <td> <p>Wave peak direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Wind speed (m/s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>DU</p> </td> <td> <p>Wind direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>t</p> </td> <td> <p>Relative time centered on the high tide of each event (min)</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p>High Tide date</p> </td> <td> <p>UTC date for scenarios corresponding to past real events</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p><strong>OUTPUTS (Y)</strong></p> </td> </tr> <tr> <td> <p>Smax</p> </td> <td> <p>Maximum flooded area during the event (m²)</p> </td> <td> <p>Post-processed scalar output</p> </td> </tr> <tr> <td> <p>Hmax</p> </td> <td> <p>Maximum water depth reached during the event (m), provided for each inland location</p> </td> <td> <p>Post-processed functional (map) output</p> </td> </tr> <tr> <td> <p>longitude</p> </td> <td> <p>Longitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>latitude</p> </td> <td> <p>Latitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>XL93</p> </td> <td> <p>Longitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>YL93</p> </td> <td> <p>Latitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><br> </p>
Ecore Metamodels and EcoreBERT Pre-trained Language Model
<p>This dataset contains ecore metamodels from the MAR dataset transformed into tree representations. The original dataset can be found here: <a href="http://mar-search.org/experiments/models20/">http://mar-search.org/experiments/models20/</a></p> <p>The data contained in this repository were used to conduct the experiments in the paper: <strong>Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models. </strong>Link to the paper: <a href="https://arxiv.org/abs/2104.01642">https://arxiv.org/abs/2104.01642</a></p> <p>The data are organized as follows:</p> <ul> <li>model : our model trained on the tree representations of metamodels with RoBERTa architecture.</li> <li>tokenizers : the byte-level BPE tokenizer we used to train our model.</li> <li>train : the training data separated into a training and validation set.</li> <li>test : the test data of all experiments conducted in the paper.</li> </ul> <p>This data repository is linked with the following Github repository containing our code: <a href="https://github.com/mweyssow/ecore-bert">https://github.com/martiwey/metamodel-concepts-bert</a></p>
Metamodel for developing learning ecosystems
<p>The ecosystems metamodel is a M2-model instantiated from MOF, a M3-model in the four-layer metamodel architecture of OMG. The main objective of this metamodel is to provide a Computing Independent Model (CIM) for describing learning ecosystems build from software components, human elements and information flows between components which are represented by web services.</p>
Ecore version of the metamodel for developing learning ecosystems
<p>The learning ecosystem metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG. The main objective of this metamodel is to provide a Computing Independent Model (CIM) for describing learning ecosystems build from software components, human elements and information flows between components which are represented by web services.<br> It is a transformation and improvement of https://doi.org/10.5281/zenodo.829859.</p>
Platform Specific Metamodel in Ecore for developing learning ecosystems
<p>The learning ecosystem metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG. The main objective of this metamodel is to provide a Platform Specific Model (PSM) for describing learning ecosystems build from Open Source software components, human elements and information flows between components which are represented by web services.</p>
A labeled Ecore metamodel dataset for domain clustering
<p>Manually labeled 555 metamodels mined from GitHub in April 2017. </p> <p>Domains: (1) bibliography, (2) conference management, (3) bug/issue tracker, (4) build systems, (5) document/office products, (6) requirement/use case, (7) database/sql, (8) state machines, (9) petri nets</p> <p>Procedure for constructing the dataset: fully manual, by searching for certain keywords and regexes (e.g. "state" and "transition" for state machines) in the metamodels and inspecting the results for inclusion. </p> <p>Format for the file names: ABSINDEX_CLUSTER_ITEMINDEX_name_hash.ecore</p>
Instance of the learning ecosystem metamodel: model of the ecosystem for knowledge management in Spanish Public Administration
<p>The conceptual model definition of the learning ecosystem for knowledge management in the Spanish Public Administration has been made by defining three views or packages from the ecosystems metamodel presented above. The views correspond to the three main parts identified in the learning ecosystem metamodel (<a href="http://doi.org/10.5281/zenodo.829859">http://doi.org/10.5281/zenodo.829859</a>): software components, human elements and the relationship among each other.</p>
Instance of the learning ecosystem metamodel: model of the ecosystem for scientific knowledge management in a PhD programme
<p>The learning ecosystem modeled from the Ecosystems Metamodel is oriented to manage the scientific knowledge generated in the scope of the PhD Program on Education in the Knowledge Society at the University of Salamanca (<a href="https://knowledgesociety.usal.es">https://knowledgesociety.usal.es</a>).</p> <p>The model (M1-level) has been divided in three packages: the ecosystem tools model; the ecosystem users model; and the ecosystem services model. Furthermore, the classes from the metamodel (M2-level) are represented to indicate which classes are used to model the example.</p>
Metadata for Historical Corpora. Realization of the Metamodel for Corpus Metadata with the help of TEI Customization
<p>TEI ODD Customization for the documentation of historical corpora:</p> <p>The TEI ODD customizations map the Metamodel for Corpus Metadata (MCM) to a TEI p5 header structure for each of the objects of the classes 'Corpus', 'Document' and 'Preparation'. The MCM is realized with a subset of the TEI p5 guidelines.</p> <p>Each ODD contains further information and explanations regarding the MCM and the customization of the TEI. Additionally, for each ODD, an HTML documentation is provided.</p>
Evaluation of the metamodel
<p>The questionnaire used is provided in the original paper: Enhancing Smart Forestry through AI-Driven Knowledge Management: A Synergistic Approach to Efficiency and Sustainability to be published in Journal of Sustainable Forestry.</p> <p><em>All values were translated from Lithuanian language and free form expressions were standardized to ensure consistent semantic interpretation.</em></p> <p> </p> <p>Variables</p> <pre><code><span>Role</span><span>:</span> <span>Years of Experience</span><span>:</span> <span>Organization Type</span><span>:</span> <span>Used KM Metamodel</span><span>:</span> <span>Ease of Use</span><span>:</span> <span>Relevance to Forestry Tasks</span><span>:</span> <span>Knowledge Retrieval Efficiency</span><span>:</span> <span>Decision Support Effectiveness</span><span>:</span> <span>Overall Satisfaction</span><span>:</span> <span>Specific Benefits</span><span>:</span> <span>Knowledge Base Improved Decision-Making</span><span>:</span> <span>Specific Instance of Decision-Making</span><span>:</span> <span>Suggestions for Improvement</span><span>:</span> <span>Additional Features Desired</span><span>:</span> <span>Enhanced Knowledge Sharing</span><span>:</span> <span>Improved Data Management</span><span>:</span> <span>Contributed to Sustainable Forestry</span><span>:</span> <span>Would Recommend to Others</span><span>:</span> <span>Additional Comments</span><span>:</span></code></pre>
Sampling data accompanying "An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model"
<p>Datasets recording sampling results, accompanying the manuscript <em>An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model, </em>Rothenberg, D. and Wang, C., submitted, GMD. Please see the included README for more details.</p>
Vortex and MetaModel Manager input files -- Exploring impacts of declining sea-ice on ice-dependent species in the Arctic
<p>Input files for Vortex PVA models and for MetaModel Manager used in publication:</p> <p><span>Lacy, Robert C., Kit M. Kovacs, Christian Lydersen, and Jon Aars</span></p> <p><span>Linking PVA models into metamodels to explore impacts of declining sea ice on ice-dependent species in the Arctic: the ringed seal, bearded seal, polar bear complex</span></p> <p> </p>
ICSME 2024 Research Track: "What Happened to my Models?" History-Aware Co-Existence and Co-Evolution of Metamodels and Models
<p> </p> <h1>ICSME 2024 Research Track: “What Happened to my Models?” History-Aware Co-Existence and Co-Evolution of Metamodels and Models</h1> <p> </p> <p>This repository provides the dataset and results for the evaluation of the paper “What Happened to my Models?” of the ICSME 2024 Research track.<br>The dataset consists of the following files:</p> <ul> <li><strong>RQ1-Type-Refactors.zip </strong>contains the operations and refactoring performed on each of the given metamodels used by our approach in RQ1.</li> <li><strong>RQ1-Type-Results.zip</strong>: contains the group results of RQ1 as shown in our paper with additional metrics of other operations not highlighted in our paper due to space limitations.</li> <li><strong>RQ2-3-PlantUML-Models.zip</strong>: contains the 250 PlantUML models and the metamodel. This folder contains the co-evolved PlantUML models, the operations performed during the co-evolution, their metrics and the state of the models bore and after the co-evolution. </li> <li><strong>RQ2-3-PlantUML-Results.zip:</strong> contains the group results of RQ2 and RQ3 as shown in our paper with additional metrics of other operations not highlighted in our paper due to space limitations</li> <li><strong>RQ2-3-FHIR-Models.zip</strong>: contains the 1180 FHIR models and the metamodel. This folder contains the co-evolved FHIR models, the operations performed during the co-evolution, their metrics and the state of the models bore and after the co-evolution.</li> <li><strong>RQ2-3-Results.zip:</strong> contains the grouped results of RQ2 and RQ3 as presented in our paper.</li> <li><strong>Additionalnformation.pdf:</strong> contains additional information on how to read the files extracted by our approach, i.e., how to read the models and operations and how our executable refactoring catlaog works since we only focused on Property Refactorings in the paper.</li> <li><strong>Results.pdf</strong>: Contains an overview of the results (the results from our paper + additional results)</li> <li><strong>Tools.zip: </strong>Contains the tools used for the evalution. For an explaination how to use it read the <strong>Additionalnformation.pdf, </strong>see below<strong> </strong>or contact the authors</li> </ul> <p><strong>Running the tools:</strong></p> <p><em>Windows 10/11<br></em><em>JDK 20 or above</em></p> <p>The tools consist of two programs: </p> <ul> <li><strong>importer.jar<br></strong>This file is used to import a FHIR or PlantUML file into the server and co-evolve it.</li> <li><strong>server_FHIR_.jar<br></strong>The server stores the models and co-evolves them. The files provided already have the metamodels preloaded, that are used in RQ2 and RQ3, i.e., FHIR_STU3 contains the FHIR metamodel version DSTU2 and STU3 and our hybrid PlantUML, while FHIR_ synthetic contains the FHIR metamodel DSTU2 and our synthetically created one.</li> </ul> <p><strong>How to use the Tools</strong></p> <p>First, start the server by starting the jar. The server also has an experimental GUI mode that allows engineers to check the types and instances that were created. <em><strong>Note: </strong>This mode is currently under development and is still unstable. The mode <em>is accessible</em> by adding -gui as a parameter.</em></p> <p><em>java -jar server_FHIR_STU3.jar -gui</em></p> <p>Otherwise, just run the server normally:</p> <p><em>java -jar server_FHIR_STU3.jar</em></p> <p>After the server has booted up, it exports InstanceTypes and operations created for the FHIR and PlantUML metamodels. Next, you start the importer. The importer has two modes: the PlantUML mode, where it imports a PlantUML state machine and co-evolves it and the FHIR mode, where it imports an FHIR file and co-evolves it into either STU3 or our synthetic version (depending on which of the preloaded servers is running). Just run the tool by providing either FHIR or PlantUML, the imported file and the path where the output should be stored.</p> <p><strong><em>FHIR-Mode:</em></strong></p> <p><em>java -jar importer.jar FHIR C:\Users\Admin\Desktop\Aaron697_Brekke496_2fa15bc7-8866-461a-9000-f739e425860a.json C:\Users\Admin\Desktop\results</em></p> <p><strong>PlantUML Mode:</strong></p> <p><em>java -jar importer.jar PlantUML C:\Users\Admin\Desktop\branch.puml C:\Users\Admin\Desktop\results</em></p> <p><em><strong>Note: </strong></em><em>Please run both the tool and the server in a command line to receive additional information about the importing and co-evolution since the tool is otherwise without a user interface.</em></p> <p> </p>
Raw data from Fast generation of a metamodel for conduction mode melt pool dimensions in Laser Powder Bed Fusion
<p>This is the raw data repository for the submission of the Manuscript <span>"Fast generation of a metamodel for conduction mode melt pool dimensions in Laser Powder Bed Fusion"</span></p>
Sample points for constructing Bivariate Cut-HDMR and CCD metamodels
<p>This supplemental file contains (1) the warpage values of 201 sample points that were used to construct the Bivariate Cut-HDMR metamodel; (2) the warpage values of 1,045 sample points that were used to construct the CCD metamodel; and (3) the Monte Carlo simulation sample points that used for validation.</p>
Toward a Metamodel Quality Evaluation Framework: Requirements, Model, Measures, and Process
<p>The quality of metamodel considerably affects the models and transformations that conform to it. Despite that, there is still little discussion about a comprehensive form to evaluate the quality of metamodels and its consequences in model-driven development processes. This paper proposes a metamodel quality evaluation framework called MQuaRE (Metamodel Quality Requirements and Evaluation). MQuaRE comprises metamodel quality requirements and measures, a quality model, and an evaluation process, with the evident influence of international standards for software product quality, such as ISO/IEC 25000 series. We present a simple use case of MQuaRE describing how requirements, measures, and the quality model should be used during the evaluation process of a metamodel for software patterns. Among other benefits, MQuaRE can help determine final metamodel quality, decide on the acceptance of a metamodel, and also assess the positive and negative aspects of a metamodel, contributing to its quality evolution.</p>
Complete diagram of the SafeConcert metamodel
<p>Complete diagram of the SafeConcert metamodel, as referenced by the following conference paper:</p> <ul> <li>L. Montecchi, B. Gallina. <strong>SafeConcert: a Metamodel for a Concerted Safety Modeling of Socio-Technical Systems.</strong> In: 5th International Symposium on Model-Based Safety and Assessment (IMBSA 2017), pp. 129-144. Trento, Italy, September 11-13, 2017.</li> </ul> <p>The image corresponds to reference [21] of the above published paper, which was previously available at <a href="http://rcl.dsi.unifi.it/%7eleonardo/safeconcert.png">http://rcl.dsi.unifi.it/~leonardo/safeconcert.png</a>.</p>
Metamodels used in the Text2VQL framework
Open the record for dataset details and reuse information.
An Architecture for Integrating Large Language Models into Metamodeling Platforms: The Example of MM-AR
Open the record for dataset details and reuse information.
Ecore version of the metamodel for information dashboards
<p>The dashboard metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG.</p>
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