A Conceptual Model for Data Storytelling Highlights in Business Intelligence Environments
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913251551346688 |
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| author | Vassiliadis, Panos Marcel, Patrick Outa, Faten El Peralta, Veronika Gkitsakis, Dimos |
| author_facet | Vassiliadis, Panos Marcel, Patrick Outa, Faten El Peralta, Veronika Gkitsakis, Dimos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00981 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Conceptual Model for Data Storytelling Highlights in Business Intelligence Environments Vassiliadis, Panos Marcel, Patrick Outa, Faten El Peralta, Veronika Gkitsakis, Dimos Databases H.2 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. |
| title | A Conceptual Model for Data Storytelling Highlights in Business Intelligence Environments |
| topic | Databases H.2 |
| url | https://arxiv.org/abs/2403.00981 |