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A Conceptual Model for Data Storytelling Highlights in Business Intelligence Environments

Panos Vassiliadis, Patrick Marcel, Faten El Outa, Veronika Peralta, Dimos Gkitsakis

Abstract

We introduce a conceptual model for highlights to support data analysis and storytelling in the domain of Business Intelligence, via the automated extraction, representation, and exploitation of highlights revealing key facts that are hidden in the data with which a data analyst works. The model builds on the concepts of Holistic and Elementary Highlights, along with their context, constituents and interrelationships, whose synergy can identify internal properties, patterns and key facts in a dataset being analyzed.

A Conceptual Model for Data Storytelling Highlights in Business Intelligence Environments

Abstract

We introduce a conceptual model for highlights to support data analysis and storytelling in the domain of Business Intelligence, via the automated extraction, representation, and exploitation of highlights revealing key facts that are hidden in the data with which a data analyst works. The model builds on the concepts of Holistic and Elementary Highlights, along with their context, constituents and interrelationships, whose synergy can identify internal properties, patterns and key facts in a dataset being analyzed.
Paper Structure (12 sections, 4 figures, 1 table)

This paper contains 12 sections, 4 figures, 1 table.

Figures (4)

  • Figure 1: The process of highlight extraction
  • Figure 2: The metamodel for highlights
  • Figure 3: Examples of holistic highlights
  • Figure 4: Examples of elementary highlights