Megamodelling and Etymology

Is a model of a model, a metamodel? Is the relational model a metamodel? Is it a model? What is a component metamodel? Is it a model of a component model? The word MODEL is subject to a lot of debates in Model Driven Engineering. Add the notion of metamodel on top of it and you will just enter what some people call the Meta-muddle. Recently megamodels have been proposed to avoid the meta-muddle. This approach is very promising but it does not solve however the primary problem. That is, even a simple use of the word Model could lead to misunderstanding and confusion. This paper tackles this problem from its very source: the polysemic nature of the word MODEL. The evolution and semantic variations of the word MODEL are modelled from many different perspectives. This papers tells how the prefix MED in indo-european has lead, five millenniums after, to the acronym MDE, and this via the word MODEL. Based on an extensive study of encyclopedias, dictionaries, thesauri, and etymological sources, it is shown that the many senses of the word MODEL can be clustered into four groups, namely model-as-representation, model-as-example, model-as-type, and model-as-mold. All these groups are fundamental to understand the real nature of Model Driven Engineering. Megamodels and Etymology are indeed keys to avoid the Meta-muddle.on

Grid Mind: Prolog-Based Simulation Environment for Future Energy Grids

Fundamental changes in the current energy grids, towards the so called smart grids, initiated a range of projects involving extensive deployment of metering and control devices into the grid infrastructure. Since in many countries, the choice of supportive information and communication technologies (ICT) for the grid devices still remains an open question, benchmarking tools aimed at predicting their behavior in the deployed solution play an essential role in the decision-making process. This paper presents a Prolog-based simulation environment, named Grid Mind, primarily intended for the very purpose. The tool was successfully used to generate simulation scenarios in several smart-grid related projects and became a self-standing simulation tool for the evaluation of information and communication technologies used to deliver lowvoltage metering and monitoring data. The tool is continuously evolving, aimed to become an integral part of the future energy grid design in the Czech Republic and beyond

National spatial data infrastructure (NSDI) of Ukraine: what are its actual, feasible and simultaneously “correct” models?

Resume. The actual, feasible and simultaneously "correct" models of digital NSDI of Ukraine are considered in the work. A model of the existed digital NSDI system of Ukraine is named “actual”. This model already differs from the model defined by the [1]. As the latter is unlikely to be implemented in the near future, the issue of the digital feasible NSDI model of Ukraine in the next five years, which would take into account the actual model, is especially acute. In addition to feasibility, such a model must also be "correct", what is proposed in the article. The correct is called a model, the truth of which can be established by inductive or deductive reasoning. To do this, the correct model must be formalized enough so that everyone can verify the authors’ reasoning independently. Understanding both actual and correct models of NSDI of Ukraine will help to properly organize and develop actual Spatial Infrastructure Activities (SpIA) in Ukraine, including the real[1] implementation of the [1]. Although the results of the article call into question its feasibility and substantiate an alternative viewpoint on the automation problem of NGDI/NSDI/SpIA. However, we are convinced that it is still possible to change the alternative viewpoint to a cooperative one, if by means of by-laws the models of NGDI (Law), NSDI (article) and, finally, SpIA are agreed upon To prove the "correctness" of the feasible NSDI model, the theory of Relational cartography and its two main methods are used: Conceptual Frameworks and Solution Frameworks. In addition, the correspondence between Relational cartography and Model-Based Engineering is used. Key words: NSDI; product model; process model; actual, feasible and «correct» model. [1] Real. 1. Which exists in reality, true. Is used with: reality, life, existence, conditions, circumstances, fact, danger, force, wages, income. 2. One that can be implemented, executed: a real plan, a real program, a real task, a real deadline. 3. Which is based on taking into account and assessing the real conditions of reality: a real approach, a real view, a real policy.- accessed 2021-feb-14, http://slovopedia.org.ua/32/53408/32016.html (Ukrainian)

On the Unification of Megamodels

Through the more and more widespread application of model-driven engineering (MDE) and the increasing diversity in applied modeling paradigms within single projects, there is an increasing need to capture not only models in isolation but also their relations.This paper is a survey on techniques capturing such relations, such as megamodels or macromodels, based on existing scientific literature. Therefore, we consider various definitions of these techniques. We further examine characteristics of the different techniques.We will propose a unified core definition of a megamodel that captures the core properties of megamodels and which can be extended to the needs of the different applications of megamodels.Finally, we give an outlook on arising application areas for megamodels

Towards strategy of geoinformation systems and technologies use for territory management

GeoInformation (GI) Systems (GIS) and GI Technologies (GIT, together GIST) have been used for almost half a century, since the creation of Canada's first GIS in the 60s of the last century, to solve territory management problems. Over the past years, GISTs have reached their maturity, but still continue to develop, covering ever wider areas of use. Even the science of geoinformatics has emerged, in which GIST is used mainly as a toolkit or technology. As an example, geoinformatics in the same Canada is called geomatics and is a technology and/or technological science. At the same time, the expansion of the field of GIST use poses to researchers the question of methods and methodology. They are followed by issues of methods and methodology of geoinformatics not only as a technology, but also as a science. Moreover, these issues become more complicated with the expansion of the field of use. In the information industry, together with the field of use, the term "domain" or "context" is used. Thus, modern GIST usage manipulate a large number of interrelated terms and concepts that are often not clearly defined. The work is devoted to the classification of the main ones, which are influenced by the strategy selected. Spatial models of territory are used in the work. They are used in the study of both territorial systems of reality and individual spatial entities and phenomena of reality. Among spatial models, the main attention is paid to information spatial models, the most famous of which are GeoInformation Systems (GIS). GIS are inseparable from GIS tools - GeoInformation Technologies (GIT). The main results of the article were obtained using the so-called method of Conceptual Frameworks (CoFr) of Spatial Information Systems (SpIS). The CoFr method is applied to a special class of GIS - Atlas Geo-Information Systems (AGIS) of large territories (LT). The AGIS class includes Electronic Atlases (EA), Atlas Information Systems (AtIS), Cartographic Information Systems (CIS) and, in fact, GIS, if we are talking about LT. AGIS-LT is a hierarchical echeloned SpIS, for which the main terms and concepts of the article are applicable. These are such terms and concepts as "strategy" and "methodology" of GIS usage. GIS, in turn, use GIT, which are also classified using CoFr SpIS. Keywords: strategy, methodology, technology, geoinformation systems (GIS), geoinformation technologies (GIT), Atlas GeoInformation System (AGIS), method of Conceptual Frameworks (CoFr

Main conceptual provisions of the creation of an electronic state register of immovable cultural heritage of Ukraine. Part 1

Annotation. To organize the creation of a new modern electronic State Register of Immovable Cultural Heritage (CH) of Ukraine, it is proposed to use a methodology based on the so-called Solutions Frameworks (SoFr) "something" = X, where X denotes both the specified system (subsystem) and class of such systems (subsystems). The application of SoFr to X entirely is called the main conceptual position 0 in the article, but despite its obviousness, the epigraph is applicable to the construction of X SoFr: “The hardest thing is to see what is right in front of you. - Goethe» [1; Preface]. X in the X SoFr record takes the meaning of a hierarchically structured Atlas Geoinformation System (AGIS), consisting of four strata (bottom-up ­): Operational (w), Application (a), Conceptual (b) and General (g). X SoFr in the article takes three meanings: SoFr AGIS1 (defines the activity of creating the first stage of AGIS - AGIS1 = X), aSoFr AGIS1 (defines the activity "between" subsystems AGIS1 Application and Operational strata top-down ¯), bSoFr AGIS1 determines the activity "between" the subsystems of AGIS1 Conceptual and Application strata from top to bottom ¯).  X SoFr is determined by the packages and the relation between them, the so-called "petrad" of Publication-Products-Processes-Basics-Services. Packages Products-Processes-Basics and the relation between them are called the main triad of SoFr. This triad is the basis of the main conceptual provisions 1-3. They are formulated as follows: SoFr.Products - provision 1, SoFr.Processes - provision 2, SoFr.Basics - provision 3. Part 1 describes the introduction to the problem and provisions 0 and 1. Provisions 2, 3 are described in Part 2. The methodology, based on the Solutions Frameworks, implements a specific systematic approach to creating a new modern electronic State Register of Immovable Cultural Heritage of Ukraine

Meta, tracer - MOF with traceability

The following document proposes a traceability solution for model-driven development. There as been already previous work done in this area, but so far there has not been yet any standardized way for exchanging traceability information, thus the goal of this project developed and documented here is not to automatize the traceability process but to provide an approach to achieve traceability that follows OMG standards, making traceability information exchangeable between tools that follow the same standards. As such, we propose a traceability meta-model as an extension of MetaObject Facility (MOF)1. Using MetaSketch2 modeling language workbench, we present a modeling language for traceability information. This traceability information then can be used for tool cooperation. Using Meta.Tracer (our tool developed for this thesis), we enable the users to establish traceability relationships between different traceability elements and offer a visualization for the traceability information. We then demonstrate the benefits of using a traceability tool on a software development life cycle using a case study. We finalize by commenting on the work developed.Orientador: Leonel Nóbreg

Migration from Legacy to Reactive Applications in OutSystems

A legacy system is an information system that significantly resists evolution. Through a migration, these systems can be moved to a more modernized environment without having to be redeveloped. OutSystems is a software company with a platform to develop and maintain applications using abstraction to increase productivity. In October 2019, OutSystems launched a new paradigm to allow developers to build reactive web applications. Because of this, the applications implemented in the old web paradigm turned into legacy systems. The OutSystems’ approach to this problem was a manual migration. However, it discards a considerable part of the effort previously made on the legacy system. A well-founded case study took place and allowed us to classify the UI as the most prioritized feature, but coincidently, the major bottleneck in migrations. So, this project had the following objectives: (1) The design and implementation of an automatic migration approach capable of converting UI elements to accelerate the manual migration; (2) The integration of the developed tool in the OutSystems platform. To transform the OutSystems paradigm’s elements, model-driven transformation rules must be set to receive the source UI elements and produce the target equivalent implementation in the new paradigm (each according to their model). However, the trans formations may not be straightforward, and a set of elements may need to be migrated to a different implementation due to Reactive Web’s best practices. Via the creation and search of UI patterns, it is possible to make special transformations for such scenarios. As a result, a migration approach was developed, allowing for the migration of UI (and other) elements. To complement this objective, the developed tool was integrated into the OutSystems platform with an easy to use interaction. Performance and usability tests proved the necessity and impact the final result had on the migration problem. This dissertation’s objectives were fully met and even exceeded, accelerating the man ual migration by providing an automatic UI conversion. This provided a quality increase in the existing process and results, giving OutSystems and its users the possibility of evolving their applications with considerable less effort and investment.Um sistema legado é um sistema de informação que resiste à evolução. Através de uma migração, estes sistemas podem ser movidos para um ambiente modernizado sem necessitar de re-implementação. A OutSystems é uma empresa de software com uma plataforma para desenvolver e manter aplicações usando abstracção para aumentar a produtividade. Em Outubro de 2019, a OutSystems lançou um novo paradigma para desenvolver aplicações reactive web. Assim, as aplicações implementadas no antigo paradigma web tornaram-se sistemas legados. A abordagem da OutSystems ao problema foi uma migração manual, no entanto, esta abordagem desconsidera uma parte significativa do investimento feito no sistema legado. Uma análise permitiu classificar a UI como a característica mais priorizada, mas também como o maior obstáculo em migrações. Assim, este projecto tem como objectivos: (1) O desenho e implementação de uma migração automática capaz de converter os elementos de UI para acelerar a migração manual; (2) A integração da ferramenta desenvolvida na plataforma da OutSystems. Para transformar os elementos dos paradigmas OutSystems, transformações de modelos têm de ser definidas para receber os elementos UI e produzir a implementação equivalente no novo paradigma (de acordo com o seu modelo). No entanto, as transformações podem não ser lineares, e um conjunto de elementos pode necessitar de uma migração para uma implementação diferente devido ao Reactive Web. Com a definição e procura de padrões de UI, é possível fazer transformações especiais para esses cenários. Como resultado, a migração foi desenvolvida, permitindo a conversão de elementos de UI (e não só). Para complementar, a ferramenta desenvolvida foi integrada na plataforma da OutSystems com uma interacção de fácil uso. Testes de desempenho e usabilidade provaram a necessidade e impacto da ferramenta no contexto da migração manual. Os objectivos desta dissertação foram completados na totalidade, acelerando a migração manual com a automação da migração de UI. Isto traz um aumento da qualidade no processo existente e nos seus resultados, dando à OutSystems e aos seus utilizadores a possibilidade de evoluírem as suas aplicações com um esforço e investimento menores

Design of a Machine Learning-based Approach for Fragment Retrieval on Models

[ES] El aprendizaje automático (ML por sus siglas en inglés) es conocido como la rama de la inteligencia artificial que reúne algoritmos estadísticos, probabilísticos y de optimización, que aprenden empíricamente. ML puede aprovechar el conocimiento y la experiencia que se han generado durante años en las empresas para realizar automáticamente diferentes procesos. Por lo tanto, ML se ha aplicado a diversas áreas de investigación, que estudian desde la medicina hasta la ingeniería del software. De hecho, en el campo de la ingeniería del software, el mantenimiento y la evolución de un sistema abarca hasta un 80% de la vida útil del sistema. Las empresas, que se han dedicado al desarrollo de sistemas software durante muchos años, han acumulado grandes cantidades de conocimiento y experiencia. Por lo tanto, ML resulta una solución atractiva para reducir sus costos de mantenimiento aprovechando los recursos acumulados. Específicamente, la Recuperación de Enlaces de Trazabilidad, la Localización de Errores y la Ubicación de Características se encuentran entre las tareas más comunes y relevantes para realizar el mantenimiento de productos software. Para abordar estas tareas, los investigadores han propuesto diferentes enfoques. Sin embargo, la mayoría de las investigaciones se centran en métodos tradicionales, como la indexación semántica latente, que no explota los recursos recopilados. Además, la mayoría de las investigaciones se enfocan en el código, descuidando otros artefactos de software como son los modelos. En esta tesis, presentamos un enfoque basado en ML para la recuperación de fragmentos en modelos (FRAME). El objetivo de este enfoque es recuperar el fragmento del modelo que realiza mejor una consulta específica. Esto permite a los ingenieros recuperar el fragmento que necesita ser trazado, reparado o ubicado para el mantenimiento del software. Específicamente, FRAME combina la computación evolutiva y las técnicas ML. En FRAME, un algoritmo evolutivo es guiado por ML para extraer de manera eficaz distintos fragmentos de un modelo. Estos fragmentos son posteriormente evaluados mediante técnicas ML. Para aprender a evaluarlos, las técnicas ML aprovechan el conocimiento (fragmentos recuperados de modelos) y la experiencia que las empresas han generado durante años. Basándose en lo aprendido, las técnicas ML determinan qué fragmento del modelo realiza mejor una consulta. Sin embargo, la mayoría de las técnicas ML no pueden entender los fragmentos de los modelos. Por lo tanto, antes de aplicar las técnicas ML, el enfoque propuesto codifica los fragmentos a través de una codificación ontológica y evolutiva. En resumen, FRAME está diseñado para extraer fragmentos de un modelo, codificarlos y evaluar cuál realiza mejor una consulta específica. El enfoque ha sido evaluado a partir de un caso real proporcionado por nuestro socio industrial (CAF, un proveedor internacional de soluciones ferroviarias). Además, sus resultados han sido comparados con los resultados de los enfoques más comunes y recientes. Los resultados muestran que FRAME obtuvo los mejores resultados para la mayoría de los indicadores de rendimiento, proporcionando un valor medio de precisión igual a 59.91%, un valor medio de exhaustividad igual a 78.95%, una valor-F medio igual a 62.50% y un MCC (Coeficiente de Correlación Matthews) medio igual a 0.64. Aprovechando los fragmentos recuperados de los modelos, FRAME es menos sensible al conocimiento tácito y al desajuste de vocabulario que los enfoques basados en información semántica. Sin embargo, FRAME está limitado por la disponibilidad de fragmentos recuperados para llevar a cabo el aprendizaje automático. Esta tesis presenta una discusión más amplia de estos aspectos así como el análisis estadístico de los resultados, que evalúa la magnitud de la mejora en comparación con los otros enfoques.[CAT] L'aprenentatge automàtic (ML per les seues sigles en anglés) és conegut com la branca de la intel·ligència artificial que reuneix algorismes estadístics, probabilístics i d'optimització, que aprenen empíricament. ML pot aprofitar el coneixement i l'experiència que s'han generat durant anys en les empreses per a realitzar automàticament diferents processos. Per tant, ML s'ha aplicat a diverses àrees d'investigació, que estudien des de la medicina fins a l'enginyeria del programari. De fet, en el camp de l'enginyeria del programari, el manteniment i l'evolució d'un sistema abasta fins a un 80% de la vida útil del sistema. Les empreses, que s'han dedicat al desenvolupament de sistemes programari durant molts anys, han acumulat grans quantitats de coneixement i experiència. Per tant, ML resulta una solució atractiva per a reduir els seus costos de manteniment aprofitant els recursos acumulats. Específicament, la Recuperació d'Enllaços de Traçabilitat, la Localització d'Errors i la Ubicació de Característiques es troben entre les tasques més comunes i rellevants per a realitzar el manteniment de productes programari. Per a abordar aquestes tasques, els investigadors han proposat diferents enfocaments. No obstant això, la majoria de les investigacions se centren en mètodes tradicionals, com la indexació semàntica latent, que no explota els recursos recopilats. A més, la majoria de les investigacions s'enfoquen en el codi, descurant altres artefactes de programari com són els models. En aquesta tesi, presentem un enfocament basat en ML per a la recuperació de fragments en models (FRAME). L'objectiu d'aquest enfocament és recuperar el fragment del model que realitza millor una consulta específica. Això permet als enginyers recuperar el fragment que necessita ser traçat, reparat o situat per al manteniment del programari. Específicament, FRAME combina la computació evolutiva i les tècniques ML. En FRAME, un algorisme evolutiu és guiat per ML per a extraure de manera eficaç diferents fragments d'un model. Aquests fragments són posteriorment avaluats mitjançant tècniques ML. Per a aprendre a avaluar-los, les tècniques ML aprofiten el coneixement (fragments recuperats de models) i l'experiència que les empreses han generat durant anys. Basant-se en l'aprés, les tècniques ML determinen quin fragment del model realitza millor una consulta. No obstant això, la majoria de les tècniques ML no poden entendre els fragments dels models. Per tant, abans d'aplicar les tècniques ML, l'enfocament proposat codifica els fragments a través d'una codificació ontològica i evolutiva. En resum, FRAME està dissenyat per a extraure fragments d'un model, codificar-los i avaluar quin realitza millor una consulta específica. L'enfocament ha sigut avaluat a partir d'un cas real proporcionat pel nostre soci industrial (CAF, un proveïdor internacional de solucions ferroviàries). A més, els seus resultats han sigut comparats amb els resultats dels enfocaments més comuns i recents. Els resultats mostren que FRAME va obtindre els millors resultats per a la majoria dels indicadors de rendiment, proporcionant un valor mitjà de precisió igual a 59.91%, un valor mitjà d'exhaustivitat igual a 78.95%, una valor-F mig igual a 62.50% i un MCC (Coeficient de Correlació Matthews) mig igual a 0.64. Aprofitant els fragments recuperats dels models, FRAME és menys sensible al coneixement tàcit i al desajustament de vocabulari que els enfocaments basats en informació semàntica. No obstant això, FRAME està limitat per la disponibilitat de fragments recuperats per a dur a terme l'aprenentatge automàtic. Aquesta tesi presenta una discussió més àmplia d'aquests aspectes així com l'anàlisi estadística dels resultats, que avalua la magnitud de la millora en comparació amb els altres enfocaments.[EN] Machine Learning (ML) is known as the branch of artificial intelligence that gathers statistical, probabilistic, and optimization algorithms, which learn empirically. ML can exploit the knowledge and the experience that have been generated for years to automatically perform different processes. Therefore, ML has been applied to a wide range of research areas, from medicine to software engineering. In fact, in software engineering field, up to an 80% of a system's lifetime is spent on the maintenance and evolution of the system. The companies, that have been developing these software systems for a long time, have gathered a huge amount of knowledge and experience. Therefore, ML is an attractive solution to reduce their maintenance costs exploiting the gathered resources. Specifically, Traceability Link Recovery, Bug Localization, and Feature Location are amongst the most common and relevant tasks when maintaining software products. To tackle these tasks, researchers have proposed a number of approaches. However, most research focus on traditional methods, such as Latent Semantic Indexing, which does not exploit the gathered resources. Moreover, most research targets code, neglecting other software artifacts such as models. In this dissertation, we present an ML-based approach for fragment retrieval on models (FRAME). The goal of this approach is to retrieve the model fragment which better realizes a specific query in a model. This allows engineers to retrieve the model fragment, which must be traced, fixed, or located for software maintenance. Specifically, the FRAME approach combines evolutionary computation and ML techniques. In the FRAME approach, an evolutionary algorithm is guided by ML to effectively extract model fragments from a model. These model fragments are then assessed through ML techniques. To learn how to assess them, ML techniques takes advantage of the companies' knowledge (retrieved model fragments) and experience. Then, based on what was learned, ML techniques determine which model fragment better realizes a query. However, model fragments are not understandable for most ML techniques. Therefore, the proposed approach encodes the model fragments through an ontological evolutionary encoding. In short, the FRAME approach is designed to extract model fragments, encode them, and assess which one better realizes a specific query. The approach has been evaluated in our industrial partner (CAF, an international provider of railway solutions) and compared to the most common and recent approaches. The results show that the FRAME approach achieved the best results for most performance indicators, providing a mean precision value of 59.91%, a recall value of 78.95%, a combined F-measure of 62.50%, and a MCC (Matthews correlation coefficient) value of 0.64. Leveraging retrieved model fragments, the FRAME approach is less sensitive to tacit knowledge and vocabulary mismatch than the approaches based on semantic information. However, the approach is limited by the availability of the retrieved model fragments to perform the learning. These aspects are further discussed, after the statistical analysis of the results, which assesses the magnitude of the improvement in comparison to the other approaches.Marcén Terraza, AC. (2020). Design of a Machine Learning-based Approach for Fragment Retrieval on Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158617TESI