50 research outputs found
PROCEEDINGS 5th PLATE Conference
The 5th international PLATE conference (Product Lifetimes and the Environment) addressed product lifetimes in the context of sustainability. The PLATE conference, which has been running since 2015, has successfully been able to establish a solid network of researchers around its core theme. The topic has come to the forefront of current (political, scientific & societal) debates due to its interconnectedness with a number of recent prominent movements, such as the circular economy, eco-design and collaborative consumption. For the 2023 edition of the conference, we encouraged researchers to propose how to extend, widen or critically re-construct thematic sessions for the PLATE conference, and the paper call was constructed based on these proposals. In this 5th PLATE conference, we had 171 paper presentations and 238 participants from 14 different countries. Beside of paper sessions we organized workshops and REPAIR exhibitions
Understanding Quantum Technologies 2022
Understanding Quantum Technologies 2022 is a creative-commons ebook that
provides a unique 360 degrees overview of quantum technologies from science and
technology to geopolitical and societal issues. It covers quantum physics
history, quantum physics 101, gate-based quantum computing, quantum computing
engineering (including quantum error corrections and quantum computing
energetics), quantum computing hardware (all qubit types, including quantum
annealing and quantum simulation paradigms, history, science, research,
implementation and vendors), quantum enabling technologies (cryogenics, control
electronics, photonics, components fabs, raw materials), quantum computing
algorithms, software development tools and use cases, unconventional computing
(potential alternatives to quantum and classical computing), quantum
telecommunications and cryptography, quantum sensing, quantum technologies
around the world, quantum technologies societal impact and even quantum fake
sciences. The main audience are computer science engineers, developers and IT
specialists as well as quantum scientists and students who want to acquire a
global view of how quantum technologies work, and particularly quantum
computing. This version is an extensive update to the 2021 edition published in
October 2021.Comment: 1132 pages, 920 figures, Letter forma
Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning
[EN] Data analysis is a key process to foster knowledge generation in particular domains
or fields of study. With a strong informative foundation derived from the analysis of
collected data, decision-makers can make strategic choices with the aim of obtaining
valuable benefits in their specific areas of action. However, given the steady growth
of data volumes, data analysis needs to rely on powerful tools to enable knowledge
extraction.
Information dashboards offer a software solution to analyze large volumes of
data visually to identify patterns and relations and make decisions according to the
presented information. But decision-makers may have different goals and,
consequently, different necessities regarding their dashboards. Moreover, the variety
of data sources, structures, and domains can hamper the design and implementation
of these tools.
This Ph.D. Thesis tackles the challenge of improving the development process of
information dashboards and data visualizations while enhancing their quality and
features in terms of personalization, usability, and flexibility, among others.
Several research activities have been carried out to support this thesis. First, a
systematic literature mapping and review was performed to analyze different
methodologies and solutions related to the automatic generation of tailored
information dashboards. The outcomes of the review led to the selection of a modeldriven
approach in combination with the software product line paradigm to deal with
the automatic generation of information dashboards.
In this context, a meta-model was developed following a domain engineering
approach. This meta-model represents the skeleton of information dashboards and
data visualizations through the abstraction of their components and features and has
been the backbone of the subsequent generative pipeline of these tools.
The meta-model and generative pipeline have been tested through their
integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully
integrated with other meta-model to support knowledge generation in learning
ecosystems, and as a framework to conceptualize and instantiate information
dashboards in different domains.
In terms of the practical applications, the focus has been put on how to transform
the meta-model into an instance adapted to a specific context, and how to finally
transform this later model into code, i.e., the final, functional product. These practical
scenarios involved the automatic generation of dashboards in the context of a Ph.D.
Programme, the application of Artificial Intelligence algorithms in the process, and
the development of a graphical instantiation platform that combines the meta-model
and the generative pipeline into a visual generation system.
Finally, different case studies have been conducted in the employment and
employability, health, and education domains. The number of applications of the
meta-model in theoretical and practical dimensions and domains is also a result itself.
Every outcome associated to this thesis is driven by the dashboard meta-model, which
also proves its versatility and flexibility when it comes to conceptualize, generate, and
capture knowledge related to dashboards and data visualizations
XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja)
[Resumen] Las Jornadas de Automática (JA) son el evento más importante del Comité Español de Automática (CEA), entidad científico-técnica con más de cincuenta años de vida y destinada a la difusión e implantación de la Automática en la sociedad. Este año se celebra la cuadragésima tercera edición de las JA, que constituyen el punto de encuentro de la comunidad de Automática de nuestro país. La presente edición permitirá dar visibilidad a los nuevos retos y resultados del ámbito, y su uso en un gran número de aplicaciones, entre otras, las energías renovables, la bioingeniería o la robótica asistencial. Además de la componente científica, que se ve reflejada en este libro de actas, las JA son un punto de encuentro de las diferentes generaciones de profesores, investigadores y profesionales, incluyendo la componente social que es de vital importancia.
Esta edición 2022 de las JA se celebra en Logroño, capital de La Rioja, región mundialmente conocida por la calidad de sus vinos de Denominación de Origen y que ha asumido el desafío de poder ganar competitividad a través de la transformación verde y digital. Pero también por ser la cuna del castellano e impulsar el Valle de la Lengua con la ayuda de las nuevas tecnologías, entre ellas la Automática Inteligente. Los organizadores de estas JA, pertenecientes al Área de Ingeniería de Sistemas y Automática del Departamento de Ingeniería Eléctrica de la Universidad de La Rioja (UR), constituyen un pilar fundamental en el apoyo a la región para el estudio, implementación y difusión de estos retos.
Esta edición, la primera en formato íntegramente presencial después de la pandemia de la covid-19, cuenta con más de 200 asistentes y se celebra a caballo entre el Edificio Politécnico de la Escuela Técnica Superior de Ingeniería Industrial y el Monasterio de Yuso situado en San Millán de la Cogolla, dos marcos excepcionales para la realización de las JA. Como parte del programa científico, dos sesiones plenarias harán hincapié, respectivamente, sobre soluciones de control para afrontar los nuevos retos energéticos, y sobre la calidad de los datos para una inteligencia artificial (IA) imparcial y confiable. También, dos mesas redondas debatirán aplicaciones de la IA y la implantación de la tecnología digital en la actividad profesional. Adicionalmente, destacaremos dos clases magistrales alineadas con tecnología de última generación que serán impartidas por profesionales de la empresa. Las JA también van a albergar dos competiciones: CEABOT, con robots humanoides, y el Concurso de Ingeniería de Control, enfocado a UAVs. A todas estas actividades hay que añadir las reuniones de los grupos temáticos de CEA, las exhibiciones de pósteres con las comunicaciones presentadas a las JA y los expositores de las empresas. Por último, durante el evento se va a proceder a la entrega del “Premio Nacional de Automática” (edición 2022) y del “Premio CEA al Talento Femenino en Automática”, patrocinado por el Gobierno de La Rioja (en su primera edición), además de diversos galardones enmarcados dentro de las actividades de los grupos temáticos de CEA.
Las actas de las XLIII Jornadas de Automática están formadas por un total de 143 comunicaciones, organizadas en torno a los nueve Grupos Temáticos y a las dos Líneas Estratégicas de CEA. Los trabajos seleccionados han sido sometidos a un proceso de revisión por pares
A Functional, Comprehensive and Extensible Multi-Platform Querying and Transformation Approach
This thesis is about a new model querying and transformation approach called FunnyQT which is realized as a set of APIs and embedded domain-specific languages (DSLs) in the JVM-based functional Lisp-dialect Clojure. Founded on a powerful model management API, FunnyQT provides querying services such as comprehensions, quantified expressions, regular path expressions, logic-based, relational model querying, and pattern matching. On the transformation side, it supports the definition of unidirectional model-to-model transformations, of in-place transformations, it supports defining bidirectional transformations, and it supports a new kind of co-evolution transformations that allow for evolving a model together with its metamodel simultaneously. Several properties make FunnyQT unique. Foremost, it is just a Clojure library, thus, FunnyQT queries and transformations are Clojure programs. However, most higher-level services are provided as task-oriented embedded DSLs which use Clojure's powerful macro-system to support the user with tailor-made language constructs important for the task at hand. Since queries and transformations are just Clojure programs, they may use any Clojure or Java library for their own purpose, e.g., they may use some templating library for defining model-to-text transformations. Conversely, like every Clojure program, FunnyQT queries and transformations compile to normal JVM byte-code and can easily be called from other JVM languages. Furthermore, FunnyQT is platform-independent and designed with extensibility in mind. By default, it supports the Eclipse Modeling Framework and JGraLab, and support for other modeling frameworks can be added with minimal effort and without having to modify the respective framework's classes or FunnyQT itself. Lastly, because FunnyQT is embedded in a functional language, it has a functional emphasis itself. Every query and every transformation compiles to a function which can be passed around, given to higher-order functions, or be parametrized with other functions
Engineering scalable modelling Languages
Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: 08-11-2019Esta tesis tiene embargado el acceso al texto completo hasta el 08-05-2021Model-Driven Engineering (MDE) aims at reducing the cost of system development by raising the level of abstraction at which developers work. MDE-based solutions frequently involve the creation of Domain-Specific Modelling Languages (DSMLs). WhilethedefinitionofDSMLsandtheir(sometimesgraphical)supportingenvironments are recurring activities in MDE, they are mostly developed ad-hoc from scratch. The construction of these environments requires high expertise by developers, which currently need to spend large efforts for their construction. This thesis focusses on the development of scalable modelling environments for DSMLs based on patterns. For this purpose, we propose a catalogue of modularity patterns that can be used to extend a modelling language with services related to modularization and scalability. More specifically, these patterns allows defining model fragmentation strategies, scoping and visibility rules, model indexing services, and scoped constraints. Once the patterns have been applied to the meta-model of a modelling language, we synthesize a customized modelling environment enriched with the defined services, which become applicable to both existing monolithic legacy models and new models. A second contribution of this thesis is a set of concepts and technologies to facilitate the creation of graphical editors. For this purpose, we define heuristics which identify structures in the DSML abstract syntax, and automatically assign their diagram representation. Using this approach, developers can create a graphical representation by default from a meta-model, which later can be customised. These contributions have been implemented in two Eclipse plug-ins called EMFSplitter and EMF-Stencil. On one hand, EMF-Splitter implements the catalogue of modularity patterns and, on the other hand, EMF-Stencil supports the heuristics and the generation of a graphical modelling environment. Both tools were evaluated in different case studies to prove their versatility, efficiency, and capabilitieEl Desarrollo de Software Dirigido por Modelos (MDE, por sus siglas en inglés) tiene como objetivo reducir los costes en el desarrollo de aplicaciones, elevando el nivel de abstracciónconelqueactualmentetrabajanlosdesarrolladores. Lassolucionesbasadas en MDE frecuentemente involucran la creación de Lenguajes de Modelado de Dominio Específico (DSML, por sus siglas en inglés). Aunque la definición de los DSMLs y sus entornos gráficos de modelado son actividades recurrentes en MDE, actualmente en la mayoría de los casos se desarrollan ad-hoc desde cero. La construcción de estos entornos requiere una alta experiencia por parte de los desarrolladores, que deben realizar un gran esfuerzo para construirlos. Esta tesis se centra en el desarrollo de entornos de modelado escalables para DSML basados en patrones. Para ello, se propone un catálogo de patrones de modularidad que se pueden utilizar para extender un lenguaje de modelado con servicios relacionados con la modularización y la escalabilidad. Específicamente, los patrones permiten definir estrategias de fragmentación de modelos, reglas de alcance y visibilidad, servicios de indexación de modelos y restricciones de alcance. Una vez que los patrones se han aplicado al meta-modelo de un lenguaje de modelado, se puede generar automáticamente un entorno de modelado personalizado enriquecido con los servicios definidos, que se vuelven aplicables tanto a los modelos monolíticos existentes, como a los nuevos modelos. Una segunda contribución de esta tesis es la propuesta de conceptos y tecnologías para facilitar la creación de editores gráficos. Para ello, definimos heurísticas que identifican estructuras en la sintaxis abstracta de los DSMLs y asignan automáticamente su representación en el diagrama. Usando este enfoque, los desarrolladores pueden crear una representación gráfica por defecto a partir de un meta-modelo. Estas contribuciones se implementaron en dos plug-ins de Eclipse llamados EMFSplitter y EMF-Stencil. Por un lado, EMF-Splitter implementa el catálogo de patrones y, por otro lado, EMF-Stencil implementa las heurísticas y la generación de un entorno de modelado gráfico. Ambas herramientas se han evaluado con diferentes casos de estudio para demostrar su versatilidad, eficiencia y capacidade
Fundamental Approaches to Software Engineering
This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution
Implicit Incremental Model Analyses and Transformations
When models of a system change, analyses based on them have to be reevaluated in order for the results to stay meaningful. In many cases, the time to get updated analysis results is critical. This thesis proposes multiple, combinable approaches and a new formalism based on category theory for implicitly incremental model analyses and transformations. The advantages of the implementation are validated using seven case studies, partially drawn from the Transformation Tool Contest (TTC)
Modelling, Reverse Engineering, and Learning Software Variability
The society expects software to deliver the right functionality, in a short amount of time and with fewer resources, in every possible circumstance whatever are the hardware, the operating systems, the compilers, or the data fed as input. For fitting such a diversity of needs, it is common that software comes in many variants and is highly configurable through configuration options, runtime parameters, conditional compilation directives, menu preferences, configuration files, plugins, etc. As there is no one-size-fits-all solution, software variability ("the ability of a software system or artifact to be efficiently extended, changed, customized or configured for use in a particular context") has been studied the last two decades and is a discipline of its own. Though highly desirable, software variability also introduces an enormous complexity due to the combinatorial explosion of possible variants. For example, the Linux kernel has 15000+ options and most of them can have 3 values: "yes", "no", or "module". Variability is challenging for maintaining, verifying, and configuring software systems (Web applications, Web browsers, video tools, etc.). It is also a source of opportunities to better understand a domain, create reusable artefacts, deploy performance-wise optimal systems, or find specialized solutions to many kinds of problems. In many scenarios, a model of variability is either beneficial or mandatory to explore, observe, and reason about the space of possible variants. For instance, without a variability model, it is impossible to establish a sampling strategy that would satisfy the constraints among options and meet coverage or testing criteria. I address a central question in this HDR manuscript: How to model software variability? I detail several contributions related to modelling, reverse engineering, and learning software variability. I first contribute to support the persons in charge of manually specifying feature models, the de facto standard for modeling variability. I develop an algebra together with a language for supporting the composition, decomposition, diff, refactoring, and reasoning of feature models. I further establish the syntactic and semantic relationships between feature models and product comparison matrices, a large class of tabular data. I then empirically investigate how these feature models can be used to test in the large configurable systems with different sampling strategies. Along this effort, I report on the attempts and lessons learned when defining the "right" variability language. From a reverse engineering perspective, I contribute to synthesize variability information into models and from various kinds of artefacts. I develop foundations and methods for reverse engineering feature models from satisfiability formulae, product comparison matrices, dependencies files and architectural information, and from Web configurators. I also report on the degree of automation and show that the involvement of developers and domain experts is beneficial to obtain high-quality models. Thirdly, I contribute to learning constraints and non-functional properties (performance) of a variability-intensive system. I describe a systematic process "sampling, measuring, learning" that aims to enforce or augment a variability model, capturing variability knowledge that domain experts can hardly express. I show that supervised, statistical machine learning can be used to synthesize rules or build prediction models in an accurate and interpretable way. This process can even be applied to huge configuration space, such as the Linux kernel one. Despite a wide applicability and observed benefits, I show that each individual line of contributions has limitations. I defend the following answer: a supervised, iterative process (1) based on the combination of reverse engineering, modelling, and learning techniques; (2) capable of integrating multiple variability information (eg expert knowledge, legacy artefacts, dynamic observations). Finally, this work opens different perspectives related to so-called deep software variability, security, smart build of configurations, and (threats to) science