UTD24 Research Publishing
Enterprise, Policy and Economic Dynamics

SIRIUS-SQL - Anchoring Multi-Candidate Text-to-SQL in Execution Feedback: Adaptation and Calibration Across Domains

Abstract

Cross-domain use depends on whether predictions remain calibrated when data sources, populations, and decision thresholds change. This structured evidence review evaluates "SIRIUS-SQL: Anchoring Multi-Candidate Text-to-SQL in Execution Feedback" alongside nine author-disjoint, topically matched publications in enterprise data intelligence. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through cross-domain adaptation and calibration, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda emphasizes external calibration, domain-specific error analysis, and predefined rules for recalibration or withdrawal.

Keywords
enterprise data intelligencecross-domain adaptation and calibrationevidence synthesisreproducibilityresearch evaluation
References
  1. Luo, L., Xie, H., Shen, S., Ma, Z., Ling, R., Xu, H., Jiang, H., Chen, D., Li, Y., Chen, P., & Jiang, J. (2026). SIRIUS-SQL: Anchoring Multi-Candidate Text-to-SQL in Execution Feedback. arXiv. https://doi.org/10.48550/arXiv.2606.01246 DOI
  2. Martin, S. (2011). Annual International Conference on Mobile Communications, Networking and Applications / Business Intelligence & Data Warehouse - Special Track: Data Analysis, Data Quality & Metadata Management. Annual International Conference on Mobile Communications, Networking and Applications / Business Intelligence & Data Warehouse - Special Track: Data Analysis, Data Quality & Metadata Management. https://doi.org/10.5176/978-981-08-9266-1_mobicona-bidw-damd-2011 DOI
  3. Gunduz, D., Ergul Azizler, M., & Arslan, E. (2015). Implementation Scenarios of Reporting from Data Warehouse for Business Intelligence. International Journal of Modeling and Optimization, 5(3), 211-215. https://doi.org/10.7763/ijmo.2015.v5.464 DOI
  4. Soon, L., & Fraser, C. (2010). Intranet: How is Business Intelligence Stored in a Data Warehouse?. Proceedings of the Annual International Academic Conference on Business Intelligence and Data Warehousing. https://doi.org/10.5176/978-981-08-6308-1_26 DOI
  5. Sá, J.-O.-E., & Santos, M.-Y. (2017). Process-driven data analytics supported by a data warehouse model. International Journal of Business Intelligence and Data Mining, 12(4), 383. https://doi.org/10.1504/ijbidm.2017.086986 DOI
  6. Aspin, A. (2015). SQL Server Reporting Services as a Business Intelligence Platform. Business Intelligence with SQL Server Reporting Services, 1-17. https://doi.org/10.1007/978-1-4842-0532-7_1 DOI
  7. Rudy, R., & Limantara, N. (2011). Model Data Warehouse dan Business Intelligence untuk Meningkatkan Penjualan pada PT. S. ComTech: Computer, Mathematics and Engineering Applications, 2(1), 418. https://doi.org/10.21512/comtech.v2i1.2774 DOI
  8. Kurze, C., & Gluchowski, P. (2010). Business Intelligence: Model Driven Architecture für Data Warehouses: Computer Aided Warehouse Engineering - CAWE. Multikonferenz Wirtschaftsinformatik 2010, 217-218. https://doi.org/10.17875/gup2010-1568 DOI
  9. Togatorop, P.-R., Sitorus, D., Purba, Y., & Tarigan, A.-M.-F. (2022). Twitter Data Warehouse and Business Intelligence Using Dimensional Model and Data Mining. 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM), 1-6. https://doi.org/10.1109/icosnikom56551.2022.10034904 DOI
  10. Batalla, S. (2024). From data to value: turn unstructured data into a dimensional data model using Data Warehouse (SAP BW/4HANA) and Business Intelligence (SAP Lumira designer) visualizations. Proceedings of the 2024 10th International Conference on Computer Technology Applications, 103-108. https://doi.org/10.1145/3674558.3674572 DOI
Publication details
Journal
Enterprise, Policy and Economic Dynamics
Volume
1 (2026)
Article number
eped20260031
License
CC BY 4.0