## 5 Discussion
On the other hand, for characterizing tasks, the typology developed by [PERSON] et al. [43] was chosen. The main reason is its simplicity and widespread use in data visualization evaluation. This taxonomy is easy to integrate into the meta-model and can be used along with the analysis goal framework to describe different analytical steps with high levels of abstraction. Because meta-models are prone to evolution, new versions can be developed with other task taxonomies or new entities that better capture the analytic activity of users, but in this case, it is necessary to take into account the impact of this changes on existing artifacts [66]. With this structuration, it was possible to design a DSL to define dashboard requirements with the aim of processing them and generating products with a configuration that supports the different tasks needed for achieving the users' information goals, fostering knowledge discovery. Another benefit of relying on a DSL is that the requirements of the dashboard are constrained and structured, allowing an easier specification of their functionalities. The DSL makes the dashboard design process more transparent for designers and unburdens them from technical and programming tasks and provides a user-friendlier approach to increase the readability and accessibility of the information held in the meta-model. The possibility of designing and generating data visualizations automatically is gaining relevance due to the democratization of data. People are continuously exposed to new information, and it is necessary to provide tools to help any user profile, including non-technical profiles, to generate knowledge and gain insights. Tailoring visualizations to specific user profiles not only aims at presenting great quantities of data in a single display, but also at conveying the information, relationships and characteristics \"hidden\" within raw datasets, taking into account the necessities of the user [10]. Current visualization generation processes focus mainly on the structure of datasets and their variables [33; 34] to develop artificial intelligence algorithms that infer proper visual encodings or visual metaphors. Focusing on datasets is crucial, because their variable types and their domains provide valuable information regarding potential visual encodings. However, there are other important user aspects to account for when dealing with information visualizations, as stated in this paper. Including this information into a generation pipeline could refine these products to increase their effectiveness and efficiency by following a user-centered approach. That is why it is important to structure tasks and goals into a meta-model and a DSL. Tasks and goals are usually conveyed through natural language, making their processing a complex activity. For example, many machine learning (ML) algorithms need structured data as an input (e.g., random forest, decision tree, linear regression, etc.). If the goal is to make a generative pipeline that yields adapted dashboards based on a ML model, a structured set of goals, tasks and users' characteristics must be provided to allow the algorithms to infer which visual elements could best match the users' needs and the users' context. By using a structured syntax to define the final user, such as the presented DSL, it is easier to train these models to seek for relationships between the users' characteristics and the effectiveness and usability of specific visual metaphors. This generation pipeline can make decision-making processes more accessible and effective for not statistically-trained people or for non-technical profiles, increasing the outcomes and benefits derived from data-driven processes. The main challenge of using AI to generate information visualizations and dashboards is the retrieval process of all the presented user dimensions to train the models, not only because several factors are involved, but because the information must be precise to map these characteristics into proper dashboard components successfully.