PREreview estructurada del Automating Business Intelligence Requirements with Generative AI and Semantic Search
- Publicado
- DOI
- 10.5281/zenodo.17220714
- Licencia
- CC BY 4.0
- Does the introduction explain the objective of the research presented in the preprint?
- Yes
- The paper clearly sets out the problem: BI requirements gathering is manual, slow, and error-prone. The objective is to automate this using generative AI + semantic search.
- Are the methods well-suited for this research?
- Somewhat appropriate
- They propose a pipeline combining LLM-based natural language processing with semantic search over schema metadata.
- Are the conclusions supported by the data?
- Somewhat supported
- Conclusions (that AI + semantic search reduce BI requirement effort) are supported by experiments, but tested in controlled settings, not in full enterprise BI projects.
- Are the data presentations, including visualizations, well-suited to represent the data?
- Somewhat appropriate and clear
- Figures are okay, but some diagrams could be clearer for non-technical BI audiences.
- How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?
- Somewhat clearly
- They suggest extending to larger datasets and real BI implementations. This is clear but could be deeper.
- Is the preprint likely to advance academic knowledge?
- Somewhat likely
- Yes, this is a novel angle (generative AI for BI requirements).
- Would it benefit from language editing?
- Yes
- Understandable but has some awkward phrasing.
- Would you recommend this preprint to others?
- Yes, but it needs to be improved
- Worth reading but still needs refinement.
- Is it ready for attention from an editor, publisher or broader audience?
- Yes, after minor changes
- Ready for attention after minor changes.
Competing interests
The author declares that they have no competing interests.