Autor(es): Almeida, Andreia ; Alves, Alberto ; Santos, Maribel Yasmina ; León, Ana ; Pires, João Moura
Data: 2024
Identificador Persistente: https://hdl.handle.net/1822/95879
Origem: RepositóriUM - Universidade do Minho
Assunto(s): Analytical requirements; Analytical visualizations; Conceptual meta-model; Model-driven analytics
Big Data is challenging analytical contexts, namely when aligning data and analytical requirements. While the capacity to collect and store new data is expanding rapidly, the pace at which it can be analyzed is developing more slowly. Defining these analytical requirements and selecting the most appropriate visualizations often depends on an in-depth understanding of what users need from the data. To address this problem, this paper proposes an assisted model-driven analytics approach to support visualization, taking domain knowledge and data as input. It allows the user to be guided in the mapping between domain concepts and available data, as well as in the translation of domain questions into analytical tasks that can be supported by useful visualizations for decision support. The approach is supported by a Meta-model that formalizes concepts needed to answer three fundamental questions, what, why, and how. This Meta-model contextualizes the data, the analytical tasks, and the supporting visualizations. The applicability of the proposal is shown through a demonstration case focused on the genome domain. The results highlight how useful visualizations are derived from the specified domain questions.
