Requirements-Driven Visualizations for Big Data Analytics: a Model-Driven approach

TL;DR

The paper tackles the challenge of non-experts selecting appropriate visual analytics for Big Data by proposing a Model Driven Architecture (MDA) framework that uses three interlinked models—CIM (User Requirements), PSM (Data Profiling), and PIM (Data Visualization)—coupled with Model-to-Model and Model-to-Text transformations to semi-automatically derive suitable visualizations and their implementations. The approach validates user needs through goal-oriented requirements and data profiling, then generates a Data Visualization Model and ultimately code (via D3.js) to produce the visual analytics; a real-world tax collection case study demonstrates end-to-end feasibility and dashboard assembly for decision support. Key contributions include formalizing a goal-oriented requirements model, a data profiling model, and a visualization model, plus a visualization metamodel and a transformation pipeline that bridges user needs and actual visual implementations. The work reduces reliance on visualization expertise, enabling decision-makers and SMEs to obtain tailored visual analytics and dashboards aligned with data characteristics and strategic goals. Overall, the framework provides a practical pathway to transform high-level information needs into concrete, implementable visual analytics in Big Data contexts, with potential for broader dashboard deployment and SME adoption.

Abstract

Choosing the right Visualization techniques is critical in Big Data Analytics. However, decision makers are not experts on visualization and they face up with enormous difficulties in doing so. There are currently many different (i) Big Data sources and also (ii) many different visual analytics to be chosen. Every visualization technique is not valid for every Big Data source and is not adequate for every context. In order to tackle this problem, we propose an approach, based on the Model Driven Architecture (MDA) to facilitate the selection of the right visual analytics to non-expert users. The approach is based on three different models: (i) a requirements model based on goal-oriented modeling for representing information requirements, (ii) a data representation model for representing data which will be connected to visualizations and, (iii) a visualization model for representing visualization details regardless of their implementation technology. Together with these models, a set of transformations allow us to semi-automatically obtain the corresponding implementation avoiding the intervention of the non-expert users. In this way, the great advantage of our proposal is that users no longer need to focus on the characteristics of the visualization, but rather, they focus on their information requirements and obtain the visualization that is better suited for their needs. We show the applicability of our proposal through a case study focused on a tax collection organization from a real project developed by the Spin-off company Lucentia Lab.

Figures (8)

• Figure 1: Overall view of the process proposed.
• Figure 2: User Requirements Metamodel.
• Figure 3: Generation of axis based visualizations from user requirements.
• Figure 4: Generation of axes for axis based visualizations from user requirements.
• Figure 5: Data Visualization Metamodel.