Visualization and Analysis of EA Models
In EA analysis activities, it is critical to make early statements and diagnosis from a high level of abstraction. Currently, this is dificult to perform and it requires both the involvement of experts and the elaboration of specialized artifacts. Furthermore, the complexity of these tasks increases with the size and level of detail of the models, as well as with the scope of domains covered.
Therefore, we are interested in finding approaches to support the analysis of big and complex models.
In contexts other than EA, it has been noticed that total / holistic / unfiltered visualizations may give insight about the models, providing analysts a starting point for exploration and general pattern discovery. The visualization of multidimensional data and visual scalability have been recurring problems for a long time, and there is a wide range of general purpose visualization methods that try to deal with these issues. In this context, we propose a set of domain-specific techniques that support these general methods, in order to help analysts in discovering outliers, patterns, and other anomalies over EA models.
As this research is in its early stages, key aspects that help analysis (such as interaction and view coordination) have been generally overlooked. Furthermore, important characteristics that are typical of EA models, such as imperfection and uncertainty, are currently not been taken into account. As a starting point for analysis, we suggest displaying the shape of the architecture as an holistic entity using a range of visual metaphors, to later drill down on elements of interest. From a concrete point of view, we want to give analysts a platform to provide visual support for the tasks that they should perform.