Capturing high-level requirements of information dashboards' components through meta-modeling

[EN]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.Tailoringcapabilities 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 structuresand 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

Tailored information dashboards: A systematic mapping of the literature

Information dashboards are extremely useful tools to exploit knowledge. Dashboards enable users to reach insights and to identify patterns within data at-a-glance. However, dashboards present a series of characteristics and configurations that could not be optimal for every user, thus requiring the modification or variation of its features to fulfill specific user requirements. This variation process is usually referred to as customization, personalization or adaptation, depending on how this variation process is achieved. Given the great number of users and the exponential growth of data sources, tailoring an information dashboard is not a trivial task, as several solutions and configurations could arise. To analyze and understand the current state-of-the-art regarding tailored information dashboards, a systematic mapping has been performed. This mapping focus on answering questions regarding how existing dashboard solutions in the literature manage the customization, personalization and/or adaptation of its elements to produce tailored displays

Impact of Automation and Visualization on a Toyota´s European process

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis dissertation focuses on implementing automation and visualization in a Toyota’s European process that ensures compliance and customer safety across Europe. The Campaign Team within Toyota Motor Europe (TME) plays a crucial role in this Request process. Given its significance to the company, it is vital to automate and optimize this process and create visualization tools to enhance effective monitoring and informed decision-making. By incorporating the Toyota Business Process (TBP) framework, we defined thirteen steps for completing this project. We analyzed existing pain points in the Request process, set clear goals, proposed a To-Be process, and involved all stakeholders in tool development. To enhance efficiency, we relied on some concepts from the Toyota Production System (TPS), including Nemawashi, Genchi Genbutsu, and Poka-yoke. As a result, we significantly reduced manual work performed by the Campaign Team every time a new request is issued. Additionally, we provided all National Marketing & Sales Companies (NMSCs) with a visual overview of the Request process. Other benefits were also implemented in this project, such as job standardization, information centralization, and enhanced communication between stakeholders and the Campaign Team. Overall, this dissertation presents a methodology that can be replicated for enhancing other Toyota’s European processes

Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

[EN]Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks to ease knowledge discovery. Choosing a wrong design could compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or configuration for each potential context, user, or data domain is a crucial task. For these reasons, there is a necessity to automatize the recommendation of visualizations and dashboard configurations to deliver tools adapted to their context. Recommendations can be based on different aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or carried out through the visualizations. This work presents a dashboard meta-model that abstracts all these factors and the integration of a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This meta-model has been used to design a domain specific language to specify dashboards requirements in a structured way. The ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any context, such as the educational context, in which a lot of data are generated, and there are several actors involved (students, teachers, managers, etc.) that would want to reach different insights regarding their learning performance or learning methodologies

Beneficios de la aplicación del paradigma de líneas de productos software para generar dashboards en contextos educativos

Data are crucial to improve decision-making and to obtain greater benefits in any type of activity. However, the large amount of information generated by new technologies has made data analysis and knowledge generation a complex task.  Numerous tools have emerged to facilitate this knowledge generation, such as dashboards. Although dashboards are very powerful tools, their effectiveness can be affected by a bad design or by not taking into account the context in which they are placed. Therefore, it is necessary to design and create tailored dashboards according to the audience and data domain. Creating tailored dashboards can be very beneficial, but also a costly process in terms of time and resources. This paper presents an application of the software product line paradigm to generate dashboards adapted to any context in a more straightforward way by reusing both software components and knowledge. One of the contexts that can be especially favored by this approach is the educational context, where analítica del aprendizaje and the analysis of student performance to improve learning methodologies are becoming very popular. Having tailored dashboards for any role (student, teacher, administrator, etc.) can improve decision making processes by showing each user the information that interests them most in the way that best enables them to understand it.Los datos son cruciales para mejorar la toma de decisiones y obtener mayores beneficios en cualquier tipo de actividad. Sin embargo, la gran cantidad de información generada debido a las nuevas tecnologías ha convertido el análisis de los datos y la generación de conocimiento a partir de ellos en una tarea compleja. Numerosas herramientas han surgido para facilitar esta generación de conocimiento, como es el caso de los dashboards o paneles de información. Aunque los paneles de control sean herramientas muy potentes, su efectividad puede verse afectada por un mal diseño o por no tener en cuenta el contexto en el que se encuadran. Por ello, es necesario diseñar y crear paneles de control a medida en función de la audiencia y dominio de los datos. Crear paneles de control personalizados puede ser muy beneficioso, pero también un proceso costoso en lo que al tiempo y recursos se refiere. Este trabajo presenta una aplicación del paradigma de líneas de productos software para generar paneles de control adaptados a cualquier contexto de manera más sencilla, reutilizando tanto componentes software como conocimiento. Uno de los contextos que puede verse especialmente favorecido por este enfoque es el contexto educativo, donde la analítica del aprendizaje y el análisis de datos sobre el rendimiento de los estudiantes se está popularizando. Contar con paneles de control personalizables para cualquier rol (estudiante, profesor, administrador, etc.) puede mejorar los procesos de toma de decisiones, mostrando a cada usuario la información que más le interesa de la forma que mejor le permita comprenderla

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