BI-Bench: A Comprehensive Benchmark Dataset and Unsupervised Evaluation for BI Systems

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Abstract

A comprehensive benchmark is crucial for evaluating automated Business Intelligence (BI) systems and their real-world effectiveness. We propose BI-Bench, a holistic, end-to-end benchmarking framework that assesses BI systems based on the quality, relevance, and depth of insights. It categorizes queries into descriptive, diagnostic, predictive, and prescriptive types, aligning with practical BI needs. Our fully automated approach enables custom benchmark generation tailored to specific datasets. Additionally, we introduce an automated evaluation mechanism within BI-Bench that removes reliance on strict ground truth, ensuring scalable and adaptable assessments. By addressing key limitations, it offers a flexible and robust, user-centered methodology for advancing next-generation BI systems.

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APA

Gupta, A., Aggarwal, A., Bithel, S., & Agarwal, A. (2025). BI-Bench: A Comprehensive Benchmark Dataset and Unsupervised Evaluation for BI Systems. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 6, pp. 1287–1299). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2025.acl-industry.90

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