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Item ModelViz: A Model-Driven Engineering Approach for Visual Analytics System Design(IEEE, 2024-03-29) Khakpour, A.; Vázquez-Ingelmo, A.; Colomo-Palacios, R.; García-Peñalvo, F. J.; Martini, A.Visual analytics systems should be able to consolidate data from disparate sources, conduct exploratory analysis, create visualizations that suit different users, and integrate seamlessly with decision-making activities to support data-driven decision-making. However, current mainstream visual analytics solutions often lack support for all these requirements. To address this gap, we propose the use of model-driven engineering to design visual analytics systems. To demonstrate the feasibility of this approach, we developed a Domain-Specific Modeling Language (DSML) named ModelViz to design visual analytics systems for consumer goods supply chain applications. Furthermore, we present the work of our DSML, using data from a manufacturing company as a case study. Finally, we evaluated ModelViz quantitatively by comparing it with other similar works from the literature. Our results demonstrate that this approach meets the requirements and provides a promising direction for designing visual analytics systems by considering domain-specific aspects to help achieve business goals.Item Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering & machine learning(Grupo GRIAL, 2022-07-26) Vázquez-Ingelmo, A.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 model-driven 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.Item A proposal to measure the understanding of data visualization elements in visual analytics applications(CEUR-WS.org, 2022-10-09) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.; Byrd, V.; Camba, J. D.Data visualizations and information dashboards are useful but complex tools. They must be fully understood to draw proper insights and to avoid misleading conclusions. However, several elements and factors are involved in this domain, which makes it difficult to learn. In previous works, we proposed a meta-model to capture the primitive elements that compose visualizations and dashboards. This meta-model has served as a framework for conducting data visualization research, but also to develop a graphical tool for generating data visualizations and dashboards. This tool (namely MetaViz) enables users to create data visualizations through fine-grained components based on the entities represented in the meta-model. The main goal of the system is to provide a learning experience in which users can freely add and configure elements to understand how they influence the final display. This work describes work in progress to validate the pedagogical value of MetaViz in terms of the understanding of data visualization concepts.Item Following up the progress of doctoral students and advisors’ workload through data visualizations: a case study in a PhD program(CEUR-WS.org, 2021-07-07) Vázquez-Ingelmo, A.; García-Holgado, A.; Hernández-Payo, H.; García-Peñalvo, F. J.; Therón, R.One of the most important aspects to consider during the development of a PhD is the students’ progress, both for their advisors and the students themselves. However, several achievements of different natures are involved during a PhD (research stays, publications, seminars, research plans, etc.). For these reasons, we propose a set of data visualizations to support decision-making processes in a PhD program. A preliminary requirement elicitation process was carried out to obtain a design basis for the implementation and integration of these tools in the PhD portal. Once the visualizations were implemented, a usability study was performed to measure the perceived usability of the newly added PhD portal functionalities. This paper presents the design process and usability study outcomes of applying data visualizations to the learning outcomes of the PhD Programme in Education in the Knowledge Society at the University of Salamanca.Item Aggregation Bias: A Proposal to Raise Awareness Regarding Inclusion in Visual Analytics(Springer, 2020-04-01) Vázquez-Ingelmo, A.; García-Peñalvo, F. J.; Therón, R.Data is a powerful tool to make informed decisions. They can be used to design products, to segment the market, and to design policies. However, trusting so much in data can have its drawbacks. Sometimes a set of indicators can conceal the reality behind them, leading to biased decisions that could be very harmful to underrepresented individuals, for example. It is challenging to ensure unbiased decision-making processes because people have their own beliefs and characteristics and be unaware of them. However, visual tools can assist decision-making processes and raise awareness regarding potential data issues. This work describes a proposal to fight biases related to aggregated data by detecting issues during visual analysis and highlighting them, trying to avoid drawing inaccurate conclusions.Item A Meta-modeling Approach to Take into Account Data Domain Characteristics and Relationships in Information Visualizations(Springer, 2021-03-30) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Therón, R.Visual explanations are powerful means to convey information to large audiences. However, the design of information visualizations is a complex task, because a lot of factors are involved (the audience profile, the data domain, etc.). The complexity of this task can lead to poor designs that could make users reach wrong conclusions from the visualized data. This work illustrates the process of identifying features that could make an information visualization confusing or even misleading with the goal of arranging them into a meta-model. The meta-model provides a powerful resource to automatically generate information visu-alizations and dashboards that take into account not only the input data, but also the audience’s characteristics, the available data domain knowledge and even the data context.