30 research outputs found

    Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review

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    This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.info:eu-repo/semantics/publishedVersio

    SIMPLIFIED GRAPHICAL DOMAIN-SPECIFIC LANGUAGES FOR THE MOBILE DOMAIN – PERSPECTIVES OF LEARNABILITY BY NONTECHNICAL USERS

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    Increasing number of technologically advanced mobile devices causes the need for seeking methods of software development that would involve persons without or with highly limited programming skills. They could participate as domain experts or individual creators of personal appli-cations. Methods based on models might be the right answer, thus the author conducted workshops and surveys concerning perspectives of gra-phical modeling languages for the mobile domain. Research revealed that nontechnical users declared high learnability of simplified ones as well as the majority of them correctly read models in such languages

    Applications of ontology in the Internet of Things: a systematic analysis

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    Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions

    Model query transformation framework- MQT: from EMF-based model query languages to persistence-spefic query languages

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    Memory problems of XML Metadata Interchange (XMI) (default persistence in Eclipse Modelling Framework (EMF)) when operating large models, have motivated the appearance of alternative mechanisms for persistence of EMF models. Most recent approaches propose using database back-ends. These approaches provide support for querying models using EMF-based model query languages (Plain EMF, Object Constraint Language (OCL), EMF Query, Epsilon Object Language (EOL), etc.). However, these languages commonly require loading in-memory all the model elements that are involved in the query. In the case of queries that traverse models (most commonly used type of queries) they require to load entire model in-memory. This loading strategy causes memory problems when operated models are large. Most database back-ends provide database-specific query languages that leverage capabilities of the database engine (better performance) and without requiring in-memory load of models for query execution (lower memory footprint). For example, Structured Query Language (SQL) is a query language for relational databases and Cypher is for Neo4J databases. In this dissertation we present MQT-Engine, a framework that supports execution of model query languages but with the e ciency (in terms of memory and performance) of a database-specifoc query language. To achieve this, MQT-Engine provides a two-step query transformation mechanism: forst, queries expressed with a model query language are transformed into a Query Language Independent Model (QLI Model); and then QLI Model is transformed into a database-specifoc query that is executed directly over the database. This mechanism provides extensibility and reusability to the framework, since it facilitates the inclusion of new query languages at both sides of the transformation. A prototype of the framework is provided. It supports transformation of EOL queries into SQL queries that are executed directly over a relational Connected Data Objects (CDO) repository. The prototype has been evaluated with two experimental evaluations. First evaluation is based on the reverse engineering domain. It compares time and memory usage required by MQT-Engine and other query languages (EMF API, OCL and SQL) to execute a set of queries over models persisted with CDO. Second evaluation is based on the railway domain, and compares performance results of MQT-Engine and other query languages (EMF API, OCL, IncQuery, SQL, etc.) for executing a set of queries. Obtained results show that MQT-Engine is able to execute successfully all the evaluated experiments. MQT-Engine is one of the evaluated solutions showing best performance results for first execution of model queries. In the case of query languages executed over CDO repositories, it is the faster solution and the one requiring less memory. For example, for the largest model in the reverse engineering case it is up to 162 times faster than a model query language executed at client-side, and it requires 23 times less memory. Additionally, the query transformation overload is constant and small (less than 2 seconds). These results validate the main goal of this dissertation: to provide a framework that gives to the model engineers the ability for specifying queries in a model query language, and then execute them with a performance and memory footprint similar to that of a persistence-specific query language. However, the framework has a set of limitations: the approach should be optimized when queries are subsequently executed; it only supports nonmodification model traversal queries; and the prototype is specific for EOL queries over CDO repositories with DBStore. Therefore, it is planned to extend the framework and address these limitations in a future version.Los problemas de memoria de XMI (mecanismo de persistencia por defecto en EMF) cuando se trabaja con modelos grandes, han motivado la aparición de mecanismos de persistencia alternativos para los modelos EMF. Los enfoques más recientes proponen el uso de bases de datos para la persistencia de los modelos. La mayoría de estos enfoques soportan la ejecución de operaciones usando lenguajes de consulta de modelos basados en EMF (EMF API, OCL, EMF Query, EOL, etc.). Sin embargo, este tipo de lenguajes necesitan almacenar en memoria al menos todos los elementos implicados en la consulta (todos los elementos del modelo en las consultas que recorren completamente el modelo consultado). Esta estrategia de carga de la información para hacer las consultas provoca problemas de memoria cuando los modelos son de gran tamaño. La mayoría de las bases de datos tienen lenguajes específicos que aprovechan las capacidades del motor de la base de datos (mayor rapidez) y sin la necesidad de cargar en memoria los modelos (menor uso de memoria). Por ejemplo, SQL es el lenguaje específico para las bases de datos relacionales y Cypher para las bases de datos Neo4J. Este trabajo propone MQT-Engine, un framework que permite ejecutar lenguajes de consulta para modelos con tiempos de ejecución y uso de memoria similares al de un lenguaje específico de base de datos. MQT-Engine realiza una transformación en dos pasos de las consultas: primero transforma las consultas que han sido escritas con un lenguaje de consulta para modelos en un modelo que es independiente del lenguaje (QLI Model); después, el modelo generado se transforma en una consulta equivalente, pero escrita con un lenguaje específico de base de datos. La transformación en dos pasos proporciona extensibilidad y reusabilidad ya que facilita la inclusión de nuevos lenguajes. Se ha implementado un prototipo de MQT-Engine que transforma consultas EOL en SQL y las ejecuta directamente sobre un repositorio CDO. El prototipo se ha evaluado con dos casos de uso. El primero está basado en el dominio de la ingeniería inversa. Se han comparado los tiempos de ejecución y el uso de memoria que necesitan MQT-Engine y otros lenguajes de consulta (EMF API, OCL y SQL) para ejecutar una serie de consultas sobre modelos persistidos en CDO. El segundo caso de uso está basado en el dominio de los ferrocarriles y compara los tiempos de ejecución que necesitan MQT-Engine y otros lenguajes (EMF API, OCL, IncQuery, etc.) para ejecutar varias consultas. Los resultados obtenidos muestran que MQT-Engine es capaz de ejecutar correctamente todos los experimentos y además es una de las soluciones con mejores tiempos para la primera ejecución de las consultas de modelos. MQTEngine es la opción más rápida y que necesita menos memoria entre los lenguajes ejecutados sobre repositorios CDO. Por ejemplo, en el caso del modelo más grande de ingeniería inversa, MQT-Engine es 162 veces más rápido y necesita 23 veces menos memoria que los lenguajes de consulta de modelos ejecutados al lado del cliente. Además, la sobrecarga de la transformación es pequeña y constante (menos de 2 segundos). Estos resultados prueban el objetivo principal de esta tesis: proporcionar un framework que permite a los ingenieros de modelos definir las consultas con un lenguaje de consulta de modelos y además ejecutarlas con una con tiempos de ejecución y uso de memoria similares a los de un lenguaje específico de bases de datos. Sin embargo, la solución tiene una serie de limitaciones: solo soporta consultas que recorren el modelo completamente y sin modificarlo; el prototipo es específico para consultas en EOL y sobre repositorios CDO (relacionales); y habría que optimizar la ejecución de las consultas cuando estas se ejecutan más de una vez. Se ha planeado resolver estas limitaciones en versiones futuras del trabajo

    Extensibility of Enterprise Modelling Languages

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    Die Arbeit adressiert insgesamt drei Forschungsschwerpunkte. Der erste Schwerpunkt setzt sich mit zu entwickelnden BPMN-Erweiterungen auseinander und stellt deren methodische Implikationen im Rahmen der bestehenden Sprachstandards dar. Dies umfasst zum einen ganz konkrete Spracherweiterungen wie z. B. BPMN4CP, eine BPMN-Erweiterung zur multi-perspektivischen Modellierung von klinischen Behandlungspfaden. Zum anderen betrifft dieser Teil auch modellierungsmethodische Konsequenzen, um parallel sowohl die zugrunde liegende Sprache (d. h. das BPMN-Metamodell) als auch die Methode zur Erweiterungsentwicklung zu verbessern und somit den festgestellten Unzulänglichkeiten zu begegnen. Der zweite Schwerpunkt adressiert die Untersuchung von sprachunabhängigen Fragen der Erweiterbarkeit, welche sich entweder während der Bearbeitung des ersten Teils ergeben haben oder aus dessen Ergebnissen induktiv geschlossen wurden. Der Forschungsschwerpunkt fokussiert dabei insbesondere eine Konsolidierung bestehender Terminologien, die Beschreibung generisch anwendbarer Erweiterungsmechanismen sowie die nutzerorientierte Analyse eines potentiellen Erweiterungsbedarfs. Dieser Teil bereitet somit die Entwicklung einer generischen Erweiterungsmethode grundlegend vor. Hierzu zählt auch die fundamentale Auseinandersetzung mit Unternehmensmodellierungssprachen generell, da nur eine ganzheitliche, widerspruchsfreie und integrierte Sprachdefinition Erweiterungen überhaupt ermöglichen und gelingen lassen kann. Dies betrifft beispielsweise die Spezifikation der intendierten Semantik einer Sprache

    Applications of ontology in the internet of things: A systematic analysis

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    Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions

    ERIGrid Holistic Test Description for Validating Cyber-Physical Energy Systems

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    Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.This work received funding in the European Community’s Horizon 2020 Program (H2020/2014–2020) under project “ERIGrid” (Grant Agreement No. 654113)

    Towards fast metamodel evolution in LiquidML

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    The software industry is applying Model-driven development approaches due to a core set of benefits, such as raising the level of abstraction and reducing coding errors. However, their underlying modeling languages tend to be quite static, making their evolution hard, specifically when the corresponding metamodel does not support primitives and/or functionalities required in specific business domains. This paper presents an extension to the LiquidML language to support fast metamodel evolution by allowing experts to abstract new language concepts from primitives while supporting automatic tool evolution and zero application downtime. To probe our claims, we evaluate the evolutionary capabilities of existing modeling languages and LiquidML in a real world language extension.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-R (POLOLAS)Ministerio de Economía y Competitividad TIN2015-71938-RED
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