Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker; their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.

In big data analytics, advanced analytic techniques operate on big datasets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in-house technical skills, the H2020 TOREADOR Project adopts a model-driven architecture for streamlining analysis processes, from data preparation to their visualization. In this article, we propose a new approach named SkyViz focused on the visualization area, in particular on (1) how to specify the user’s objectives and describe the dataset to be visualized, (2) how to translate this specification into a platform-independent visualization type, and (3) how to concretely implement this visualization type on the target execution platform. To support step (1), we define a visualization context based on seven prioritizable coordinates for assessing the user’s objectives and conceptually describing the data to be visualized. To automate step (2), we propose a skyline-based technique that translates a visualization context into a set of most suitable visualization types. Finally, to automate step (3), we propose a skyline-based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on one hand, more visualization coordinates on the other. The article is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project.