Avalilação PREreview Estruturada de SiriusBI: A Comprehensive LLM-Powered Solution for Data Analytics in Business Intelligence
- Publicado
- DOI
- 10.5281/zenodo.17220731
- Licença
- CC BY 4.0
- Does the introduction explain the objective of the research presented in the preprint?
- Yes
- The paper clearly states its goal: to design an end-to-end BI system that integrates LLMs for query understanding, semantic modeling, and automated reporting.
- Are the methods well-suited for this research?
- Somewhat appropriate
- They propose a modular pipeline: LLM-based natural language query interpretation, schema alignment, and auto-report generation. Strong approach, but evaluation is more prototype-level.
- Are the conclusions supported by the data?
- Somewhat supported
- Their demo cases show LLMs improve flexibility in BI workflows. Results support the claims but remain limited to lab environments.
- Are the data presentations, including visualizations, well-suited to represent the data?
- Somewhat appropriate and clear
- Diagrams illustrate architecture well, but figures could be simplified.
- How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?
- Somewhat clearly
- Authors discuss extending SiriusBI to enterprise settings and scaling with more diverse datasets. Next steps are suggested, but more detail on risks (e.g., hallucinations, cost) would help.
- Is the preprint likely to advance academic knowledge?
- Somewhat likely
- Novel: few works connect end-to-end BI pipelines with LLMs. Incremental in terms of architecture, but meaningful.
- Would it benefit from language editing?
- Yes
- Mostly readable, with some awkward phrasing.
- Would you recommend this preprint to others?
- Yes, but it needs to be improved
- Is it ready for attention from an editor, publisher or broader audience?
- Yes, after minor changes
Competing interests
The author declares that they have no competing interests.