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Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34
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Item Fostering Decision-Making Processes in Health Ecosystems Through Visual Analytics and Machine Learning(Springer, 2022-06-26) García-Peñalvo, F. J.; Vázquez-Ingelmo, A.; García-Holgado, A.Data-intensive contexts, such as health, use information systems to merge, synthesize, represent, and visualize data by using interfaces to ease decision-making processes. All data management processes play an essential role in exploiting data’s strategic value from acquisition to visualization. Technologi-cal ecosystems allow the deployment of highly complex services while supporting their evolutionary nature. However, there is a challenge regarding the design of high-level interfaces that adapt to the evolving nature of data. The AVisSA project is focused on tackling the development of an automatic dashboard generation system (meta-dashboard) using Domain Engineering and Artificial Intelligence techniques. This approach makes it possible to obtain dashboards from data flows in technological ecosystems adapted to specific domains. The implementation of the meta-dashboard will make intensive use of user experience testing throughout its development, which will allow the involvement of other actors in the ecosystem as stakeholders (public administration, health managers, etc.). These actors will be able to use the data for decision-making and design improvements in health provision.Item Advances in the use of domain engineering to support feature identification and generation of information visualizations(ACM, 2020-10-21) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Information visualization tools are widely used to better understand large and complex datasets. However, to make the most out of them, it is necessary to rely on proper designs that consider not only the data to be displayed, but also the audience and the context. There are tools that already allow users to configure their displays without requiring programming skills, but this research project aims at exploring the automatic generation of information visualizations and dashboards in order to avoid the configuration process, and select the most suitable features of these tools taking into account their contexts. To address this problem, a domain engineering, and machine learning approach is proposed.Item Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning(ACM, 2019-10-16) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Information dashboards are sophisticated tools. Although they enable users to reach useful insights and support their decision-making challenges, a good design process is essential to obtain powerful tools. Users need to be part of these design processes, as they will be the consumers of the information displayed. But users are very diverse and can have different goals, beliefs, preferences, etc., and creating a new dashboard for each potential user is not viable. There exist several tools that allow users to configure their displays without requiring programming skills. However, users might not exactly know what they want to visualize or explore, also becoming the configuration process a tedious task. This research project aims to explore the automatic generation of user interfaces for supporting these decision-making processes. To tackle these challenges, a domain engineering, and machine learning approach is taken. The main goal is to automatize the design process of dashboards by learning from the context, including the end-users and the target data to be displayed.Item Connecting domain-specific features to source code: Towards the automatization of dashboard generation(2019-10-31) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.; Amo-Filvà, D.; Fonseca-Escudero, D.Dashboards are useful tools for generating knowledge and support decision-making processes, but the extended use of technologies and the increasingly available data asks for user-friendly tools that allow any user profile to exploit their data. Building tailored dashboards for any potential user profile would involve several resources and long development times, taking into account that dashboards can be framed in very different contexts that should be studied during the design processes to provide practical tools. This situation leads to the necessity of searching for methodologies that could accelerate these processes. The software product line paradigm is one recurrent method that can decrease the time-to-market of products by reusing generic core assets that can be tuned or configured to meet specific requirements. However, although this paradigm can solve issues regarding development times, the configuration of the dashboard is still a complex challenge; users' goals, datasets, and context must be thoroughly studied to obtain a dashboard that fulfills the users' necessities and that fosters insight delivery. This paper outlines the benefits and a potential approach to automatically configuring information dashboards by leveraging domain commonalities and code templates. The main goal is to test the functionality of a workflow that can connect external algorithms, such as artificial intelligence algorithms, to infer dashboard features and feed a generator based on the software product line paradigmItem Capturing high-level requirements of information dashboards’ components through meta-modeling(ACM, 2019-10-16) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Information dashboards are increasing their sophistication to match new necessities and adapt to the high quantities of generated data nowadays. These tools support visual analysis, knowledge generation, and thus, are crucial systems to assist decision-making processes. However, the design and development processes are complex, because several perspectives and components can be involved. Tailoring capabilities are focused on providing individualized dashboards without affecting the time-to-market through the decrease of the development processes' time. Among the methods used to configure these tools, the software product lines paradigm and model-driven development can be found. These paradigms benefit from the study of the target domain and the abstraction of features, obtaining high-level models that can be instantiated into concrete models. This paper presents a dashboard meta-model that aims to be applicable to any dashboard. Through domain engineering, different features of these tools are identified and arranged into abstract structures and relationships to gain a better understanding of the domain. The goal of the meta-model is to obtain a framework for instantiating any dashboard to adapt them to different contexts and user profiles. One of the contexts in which dashboards are gaining relevance is Learning Analytics, as learning dashboards are powerful tools for assisting teachers and students in their learning activities. To illustrate the instantiation process of the presented meta-model, a small example within this relevant context (Learning Analytics) is also provided.Item Taking advantage of the software product line paradigm to generate customized user interfaces for decision-making processes: A case study on university employability(2019-07-01) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.University employment and, specifically, employability has gained relevance since research in these fields can lead to improvement in the quality of life of individual citizens. However, empirical research is still insufficient to make significant decisions, and relying on powerful tools to explore data and reach insights on these fields is paramount. Information dashboards play a key role in analyzing and visually exploring data about a specific topic or domain, but end users can present several necessities that differ from each other, regarding the displayed information itself, design features and even functionalities. By applying a domain engineering approach (within the software product line paradigm), it is possible to produce customized dashboards to fit into particular requirements, by the identification of commonalities and singularities of every product that could be part of the product line. Software product lines increase productivity, maintainability and traceability regarding the evolution of the requirements, among other benefits. To validate this approach, a case study of its application in the context of the Spanish Observatory for University Employability and Employment system has been developed, where users (Spanish universities and administrators) can control their own dashboards to reach insights about the employability of their graduates. These dashboards have been automatically generated through a domain specific language, which provides the syntax to specify the requirements of each user. The domain language fuels a template-based code generator, allowing the generation of the dashboards' source code. Applying domain engineering to the dashboards' domain improves the development and maintainability of these complex software products given the variety of requirements that users might have regarding their graphical interfaces.Item Domain engineering for generating dashboards to analyze employment and employability in the academic context(ACM, 2018-10-24) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Data analysis is a key process to foster knowledge generation regarding 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. Dashboards offer a software solution for visually analyzing large volumes of data in order 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. Having a methodology to efficiently generate dashboards taking into account differing needs would add a customization layer to allow particular users to reach their own goals. This approach can be achieved through domain engineering and automatic code generation processes. This paper presents the application of domain engineering within the dashboards’ domain through a case study in the context of the Spanish Observatory for University Employment and Employability, in which a set of dashboards can be generated to exploit different perspectives of employment and employability data in the academic context.