Abstract
Data are crucial to improve decision-making and 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 generation of knowledge, such as dashboards. Although dashboards are useful tools, their effectiveness can be affected by poor design or by not taking into account the context in which they are placed. Therefore, it is necessary to design and create custom dashboards according to the audience and data domain. This paper presents an application of the software product line paradigm and the integration of this approach into a web service to allow users to request source code for customized information dashboards. The main goal is to introduce the idea of creating a holistic ecosystem of different services to craft and integrate information visualizations in a variety of contexts. One of the contexts that can be especially favored by this approach is the educational context, where learning analytics, data analysis of student performance, and didactic tools are becoming very relevant. Three different use cases of this approach are presented to illustrate the benefits of the developed generative service.
Highlights
The Model-Driven Development (MDD) approach can be implemented through the model-driven architecture (MDA), a guideline proposed by the Object Management Group (OMG)
The OMG proposal determines a set of standards to develop the approach, such as meta-object facility (MOF), unified modeling language (UML), XML (Extensible Markup Language), metadata interchange (XMI), and query/view/transformation (QVT)
This work provides the foundation for designing an ecosystem for developing information dashboards based on different services with different well-defined functionalities
Summary
They support the understanding of complex datasets and are applicable to a variety of contexts and data domains. Information dashboards provide support to learn from data and can be considered educational tools [1]. The automatic generation and design of dashboards is a popular research topic, given its potential benefits for exploiting datasets. This generative process can be pursued through different methodologies and paradigms. Configuration wizards could be complemented with visual mapping methods to assist the users in the selection of visualization types taking into account the data types or structure [13,14,15,16]
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